NK cells immunoediting
Aleksandr Bykov + code from Peter Repiscak
17 July 2023
Project description
Uncovering immune evasion mechanisms of leukemic cells from natural killer cells. In this sub-project RNA sequencing of leukemic cells is used to discover transcriptional changes resulting in NK cell-resistance. There is another sub-project using ATAC-seq (transposase-accessible chromatin) to epigenetic changes resulting in NK cell-resistance - this is not part of this report.
Experimental design
Taken from discussion summary (‘21009_Porject discussion summary.docx’):
- 4 different cell lines (p185#13G, p185#13H, p185#15M, p185#15O). ADD description about cell line differences.
- All conditions in technical triplicates and 2 biological replicates (biological replicates can be added any time, if there is a need).
- Technical replicate means that we already start the co-culture in a separate well.
Biological and technical replicates
Timeline of the experiment
- On time point Day 4 we FACS sort the original tumor cells (like Day 0) and the tumor cells which were co-cultured for 4 days with NK cells for the following analyses: ATAC seq, RNA seq and barcode analysis. Additionally we co-culture a certain amount of the same tumor cells again with NK cells for the analysis of Day 10.
- On time point Day 10 we FACS sort the tumor cells which were co-cultured for 10 days with NK cells for the same analyses: ATAC seq, RNA seq and barcode analysis.
Experiment Timepoints
RNA-seq data analysis
RNA-seq methods: QuantSeq 3’ mRNA-Seq - 1x50bp HiSeq 3000/4000.
Pre-processing: ADD methods+versions for preprocessing 
Downstream analysis: ADD methods+versions for downstream!
MultiQC report ADD full multiqc report description. Few observations:
- 3’ bias (expected for Quant-seq)
- EXP9.8 has longer reads (?) 120bp as opposed to 51bp for every other experiment. This is also reflected in a higher alignment to coding region. Further, a clear separation of EXP9.8 from other experiments is observed on the PCA plot. However, also EXP9.7 is somehow separated (along the PC2) from the other experiments. The quantification and alignment were done with the same settings all the experiments
# importing only key functions that are actually used - not to polute namespace!
import::from(readr, read_csv)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup) # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = SummarizedExperiment, colData, assay) # used in every .Rmd
import::from(.from = tableone, CreateTableOne)Preparing datasets
# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup) # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = DESeq2, .all=TRUE)
import::from(.from = tximport, tximport)
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filteringexperiment_name="nk_tum_immunoedit_complete" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1" # used only to construct initial dds object
abs_filt_samples=3
# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
#biomart_host = "https://www.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"
# directories
output_dir = paste0("results_dir", "/",experiment_name,"/")
dir.create(output_dir)Preparing metadata and expression data and combining into a dds (DESeqDataSet object) that is ready for analyses. dds object is further pre-filtered to remove lowly expressed genes (FPM (fragments per million mapped fragments) > 1 in at least 3 samples). Salmon quantification (quant.sf) transcript abundance estimates (transcript expression) is imported and converted to gene expression using tximport.
# Preparing metadata
rnaseq_metadata_file <- here::here(config$metadata$rnaseq$rnaseq_metadata)
rnaseq_metadata_df_raw <- readr::read_csv(file = rnaseq_metadata_file) # 133 samples; 42 samples from other project?
quantseq_files <- here::here(config$metadata$rnaseq$rnaseq_salmon_files)
# salmon quant files
quant_files_md5sums_raw <- readr::read_table(file = quantseq_files, col_names = c("md5sum", "file_path"))
quant_files_md5sums <- quant_files_md5sums_raw %>%
dplyr::mutate(absolute_quant_files_path = gsub(pattern = "\\./", replacement = paste0(dirname(quantseq_files), "/"), file_path),
sample_name = gsub(pattern = "(\\./)(S_.+)(_0_.+)", replacement = "\\2", file_path)) %>%
# for batch3
dplyr::mutate(sample_name = gsub(pattern = "(\\./)(R.+)(_S.+)", replacement = "\\2", sample_name)) %>%
# EK were samples from other experiment - they were removed from quant.sf list, but in case remove them here as well!
dplyr::filter(!grepl(pattern = "\\./EK.+", x = sample_name)) %>%
dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(S_.+)(_quant/quant.sf)", replacement = "\\2", x = file_path)) %>%
# for batch 3
dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(R.+_S.+)(_quant/quant.sf)", replacement = "\\2", x = bsf_sample_name))
# experiment info - match experiment to run
experiment_info_batch1_2_raw <- readr::read_csv("datasets/metadata/rnaseq_merge_sample_sheet.csv", col_types =cols(.default = "c"))
experiment_info_batch3_raw <- readr::read_csv("datasets/metadata/rnaseq_sample_sheet_batch3.csv", col_types = cols(.default = "c"))
experiment_info_raw <- dplyr::bind_rows(experiment_info_batch1_2_raw, experiment_info_batch3_raw)
# EK samples are from different experiment
experiment_info <- experiment_info_raw %>%
dplyr::filter(!grepl(pattern = "EK.+", x = original_sample_name)) %>%
dplyr::rename(library_name = experiment,
bsf_sample_name = sample_name) %>%
# fixing library_name for experiment EXP9.8
dplyr::mutate(library_name = dplyr::if_else(library_name == "BSF_1241", "R0128_L5464", library_name)) %>%
dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=original_sample_name) ~ paste0(original_sample_name, "_", library_name),
TRUE ~ paste0("S_", original_sample_name, "_", library_name)))
# loading metadata and adding experiment and salmon data
rnaseq_metadata_df <- rnaseq_metadata_df_raw %>%
dplyr::mutate(condition_complete = condition) %>%
dplyr::mutate(condition = gsub(pattern = "(.+)(_Day.+)", replacement = "\\1", condition_complete)) %>%
dplyr::mutate(condition = gsub(pattern = "\\+", replacement = "_plus_", condition),
timepoint_cell_harvesting = gsub(pattern = " ", replacement = "_", timepoint_cell_harvesting),
experiment = gsub(pattern = " ", replacement = "", original_experiment)) %>%
dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=sample_name) ~ paste0(sample_name, "_", library_name),
TRUE ~ paste0("S_", sample_name, "_", library_name)))
quant_files_md5sums_annot <- quant_files_md5sums %>%
dplyr::left_join(., experiment_info, by = "bsf_sample_name") %>%
dplyr::select(-sample_name, -library_name)
rnaseq_metadata_df_annot <- rnaseq_metadata_df %>%
dplyr::left_join(., quant_files_md5sums_annot, by="sample_name_unique") %>%
dplyr::mutate(original_sample_name = sample_name) %>%
dplyr::mutate(sample_name = sample_name_unique) %>%
dplyr::mutate(filenames = sample_name_unique)
#table(rnaseq_metadata_df_annot$original_experiment)
#table(rnaseq_metadata_df_annot$cell_line_label)
rnaseq_metadata_df_annot_reduced <- rnaseq_metadata_df_annot %>%
#dplyr::filter(original_experiment == "EXP9_6") %>%
#dplyr::filter(condition %in% c("Tumor_only", "Tumor_plus_NK_Day14")) %>%
dplyr::mutate(technical_replicate = factor(technical_replicate, levels = c("1", "2", "3")),
condition = factor(condition, levels = c("Tumor_only", "Tumor_plus_NK", "Tumor_plus_IFNg", "Tumor_plus_WT_NK", "Tumor_plus_PrfKO_NK")),
timepoint_cell_harvesting = factor(timepoint_cell_harvesting, levels = c("timepoint_0", "timepoint_1", "timepoint_2")),
cell_line_label = as.factor(cell_line_label),
experiment = factor(experiment, levels = c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7", "EXP9.8")),
condition_tp = paste0(condition,"_",timepoint_cell_harvesting)) %>%
dplyr::mutate(condition_tp = factor(condition_tp, levels = c("Tumor_only_timepoint_0",
"Tumor_only_timepoint_1", "Tumor_only_timepoint_2",
"Tumor_plus_NK_timepoint_1", "Tumor_plus_NK_timepoint_2",
"Tumor_plus_WT_NK_timepoint_2",
"Tumor_plus_IFNg_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"))) %>%
#fixing day_cell_harvesting
dplyr::mutate(day_cell_harvesting = gsub(pattern = " ", replacement = "", day_cell_harvesting)) %>%
dplyr::mutate(day_cell_harvesting = factor(day_cell_harvesting, levels=c("Day0", "Day4", "Day6", "Day10", "Day14", "Day16")))
metadata <- rnaseq_metadata_df_annot_reduced #cond_data
#rm(reseq_metadata)
rownames(metadata) <- metadata$filenames
# use alternative sample names for multiQC
# renaming for the multiQC report
# metadata_multiqc <- metadata %>%
# dplyr::select(bsf_sample_name, experiment, technical_replicate, cell_line_label, condition, timepoint_cell_harvesting) %>%
# dplyr::mutate(cell_line_label = paste0("cl", cell_line_label),
# condition = gsub(pattern = "Tumor", replacement = "T", x = condition),
# timepoint_cell_harvesting = gsub(pattern = "timepoint", replacement = "tp", x=timepoint_cell_harvesting),
# technical_replicate = paste0("repl",technical_replicate)) %>%
# dplyr::mutate(alt_name = paste(experiment, cell_line_label, technical_replicate, condition, timepoint_cell_harvesting, sep="-")) %>%
# dplyr::select(bsf_sample_name, alt_name)
#
# readr::write_tsv(metadata_multiqc, file = paste0(OUTPUT_DIR, experiment_name, "_metadata_multiqc.tsv"))#TODO:
# [ ] - add md5sums for tx2gene_file
gtf_file <- file.path(annotation_dir, "Mus_musculus.GRCm38.102.gtf") # needed only once to generate tx2gene.tsv
tx2gene_file <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.tsv")
tx2gene_file_md5sum <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.md5sum")
if (!file.exists(tx2gene_file)) {
message(tx2gene_file, " does not exist: generating!")
txDB <- GenomicFeatures::makeTxDbFromGFF(gtf_file, format="gtf")
# AnnotationDbi::keys
tx_name <- annot_db_keys(txDB, keytype="TXNAME")
# AnnotationDbi::select
tx2gene_gtf <- annot_db_select(txDB,
keys = tx_name,
columns="GENEID",
keytype = "TXNAME")
# saving tx2gene into destdir
write.table(tx2gene_gtf,
file = tx2gene_file,
sep = "\t",
col.names = FALSE,
row.names = FALSE,
quote = FALSE)
tx2gene_md5sum <- digest::digest(tx2gene_file, file=TRUE, algo="md5")
write.table(paste(tx2gene_md5sum, basename(tx2gene_file), collapse = "\t"),
file = tx2gene_file_md5sum,
sep = "\t",
col.names = FALSE,
row.names = FALSE,
quote = FALSE)
} else {
message(tx2gene_file, " exists: loading!")
tx2gene <- readr::read_tsv(tx2gene_file,
col_names = c("tx_id", "gene_id"),
show_col_types = FALSE)
}precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
if(!file.exists(precomputed_objects_file)){
cat("RNA objects file '", precomputed_objects_filename, "' does not exist in the OUTPUT_DIR...\n")
cat("generating", precomputed_objects_filename, "\n")
# Generating dds and filtered dds objects ----
#require(DESeq2)
# here there is a single sample so we use ~1.
# expect a warning that there is only a single sample...
# generating dds object ----
# txi_object <- generateTxi(metadata_object=metadata,
# column_names=c("absolute_quant_files_path", "filenames"),
# tx2gene = tx2gene)
quant_files <- metadata$absolute_quant_files_path
names(quant_files) <- metadata$sample_name
txi_object <- tximport::tximport(quant_files,
type="salmon",
tx2gene = tx2gene,
ignoreTxVersion = TRUE)
sum(rownames(metadata) == colnames(txi_object$counts)) # check if names match between metadata and counts data
cat("... txi_object\n")
dds <- DESeq2::DESeqDataSetFromTximport(txi_object,
colData = metadata,
design = as.formula(paste0("~", experiment_design)))
cat("... dds\n")
dds <- DESeq2::estimateSizeFactors(dds)
dds <- DESeq2::DESeq(dds, minReplicatesForReplace=Inf) # do not replace outliers based on replicates
# filtering dds
cat("... dds filtering\n")
dds_filt <- filterDatasets(dds, abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
dds_filt <- DESeq2::estimateSizeFactors(dds_filt)
# Generate annotation file using biomaRt
#FIXME:
# Issues with Curl when using https:// and http:// throws a warning:
# Warning: Ensembl will soon enforce the use of https.
# Ensure the 'host' argument includes "https://"
cat("... fetching annotation from biomart\n")
ensemblAnnot <- generateEnsemblAnnotation(ensembl_ids = rownames(dds),
host=biomart_host,
version=biomart_Ens_version,
dataset=biomart_dataset)
cat("saving...", precomputed_objects_file, "\n")
cat("...into:", output_dir, "\n")
save(dds, dds_filt, ensemblAnnot, file = precomputed_objects_file)
} else{
cat("RNA objects file '", precomputed_objects_filename, "' exist...loading\n")
load(precomputed_objects_file)
}## RNA objects file ' nk_tum_immunoedit_complete_dds_objects.RData ' exist...loading#transformation after filtering
if(!file.exists(precomputed_transf_objects_file)){
# transformation
log2_norm_filt <- DESeq2::normTransform(dds_filt)
vsd_filt <- DESeq2::vst(dds_filt, blind = TRUE) # blind = TRUE for QC
#rld_filt <- rlog(dds_filt, blind = TRUE)
#rld_filt - too big for >100 samples; skipping!
#rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
save(log2_norm_filt, vsd_filt,
file = precomputed_transf_objects_file)
} else{
load(precomputed_transf_objects_file)
}Parameters for dataset preparation:
| Parameter | Value | Decription |
|---|---|---|
| experiment_name | nk_tum_immunoedit_complete | this can be a subset analysis - e.g. just batch1,… |
| experiment_design | 1 | used only to construct initial dds object |
| abs_filt_samples | 3 | # at least in N samples, which is a smallest group size |
| biomart_host | http://nov2020.archive.ensembl.org | |
| biomart_Ens_version | Ensembl Genes 102 | |
| biomart_dataset | mmusculus_gene_ensembl | |
Objects overview:
dds_objects_overview <- data.frame(dds_object = c("dds", "dds_filt"),
n_samples = c(ncol(dds), ncol(dds_filt)),
n_genes = c(nrow(dds), nrow(dds_filt)))
dds_objects_overview %>%
kableExtra::kbl(caption = "dds objects overview") %>%
kableExtra::kable_classic(full_width = T)| dds_object | n_samples | n_genes |
|---|---|---|
| dds | 169 | 51149 |
| dds_filt | 169 | 15430 |
# DT::datatable(dds_objects_overview,
# options = list(pageLength = 10),
# caption = 'Table 1: Samples in validation set')Exploratory Data Analysis - metadata
#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = DT, datatable)
import::from(.from = ggplot2, .all=TRUE)
import::from(.from = DataExplorer, plot_bar)
#import::from(.from = tableone, CreateTableOne)
import::from(.from = dplyr, group_by, count)experiment_name="nk_tum_immunoedit_complete"
output_dir <- paste0(results_dir, "/",experiment_name,"/") # "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/" #
# results_dir defined in the nk_immunoediting.Rmd file
eda_plots_dir <- paste0(output_dir, "eda_plots/")
dir.create(eda_plots_dir)
# raw and filtered dds
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData") #"nk_tum_immunoedit_complete_dds_objects.RData" #
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# log2 and vsd transformed
precomputed_transf_objects_filename <- "nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# check if object loaded if not load
if(!exists(precomputed_objects_file)){
load(precomputed_objects_file)
load(precomputed_transf_objects_file)
}
metadata <- tibble::as_tibble(SummarizedExperiment::colData(dds_filt))
key_metadata <- metadata %>%
dplyr::select(experiment, condition_tp,condition, timepoint_cell_harvesting, day_cell_harvesting, cell_line_label, technical_replicate)
# saving metadata
openxlsx::write.xlsx(metadata, file = file.path(output_dir, paste0(experiment_name, "_metadata.xlsx")))Metadata overview:
# 'KeyTable', ,'FixedHeader', 'Responsive', 'Scroller', 'ColReorder'
# to add show entries - https://stackoverflow.com/questions/64976684/how-to-have-buttons-and-a-show-entries-using-datatables-dt-in-rmarkdown
DT::datatable(data = metadata,
caption = 'Metadata overview',
filter = 'top',
class = 'hover',
extensions = c('Buttons'), options = list(
pageLength = 5,
#autoWidth = TRUE
#fixedHeader = TRUE,
scrollX = TRUE,
#scrollY = TRUE,
#scrollY = 200,
#deferRender = TRUE,
#scroller = TRUE,
#colReorder = TRUE,
#keys = TRUE,
searchHighlight = TRUE,
dom = 'Blfrtip',
buttons = list('copy', 'colvis', list(
extend = 'collection',
buttons = c('csv', 'excel', 'pdf'),
text = 'Download'
))
)
)# 1. metadata overview
# 2. composition per experiment
# 3. relationship (table(x, y)) - to see how many variables we have in each category; data imbalance!?
# frequency of key variables ----
# fig.show='hide' - to supress plotting here
key_variables_freq <- DataExplorer::plot_bar(key_metadata, order_bar=TRUE, ggtheme = theme_bw(), title = "Frequency of key variables")# table of variable frequency per experiment ----
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata, -experiment)),
strata = c("experiment"),
data = key_metadata)
# relate variables ----
# cat_cat_plot <- ggplot(data = key_metadata, color=cell_line_label) +
# geom_count(mapping = aes(x = experiment, y = condition_tp)) #+ facet_wrap(~cell_line_label)
cat_cat_counts <- key_metadata %>%
dplyr::group_by(cell_line_label) %>%
dplyr::count(experiment, condition_tp)
cat_cat_plot <- ggplot(data = cat_cat_counts, mapping = aes(x = experiment, y = condition_tp, group=cell_line_label)) +
# geom_tile(mapping = aes(fill = n))
geom_point(mapping=aes(size=n, color=cell_line_label), position = position_dodge(width=0.5)) +
scale_size(breaks = c(0,1,2,3,4,5,6)) +
# ggrepel::geom_label_repel(aes(label = n),
# box.padding = 0.35,
# point.padding = 0.5,
# segment.color = 'grey50') +
ggtitle("Number of samples pre condition/experiment.") +
theme_bw()Frequency of key variable:
print(key_variables_freq$page_1)
ggsave(filename = paste0(eda_plots_dir, "key_variables_freq_plot.png"),
plot=key_variables_freq$page_1,
width = 20, height = 20, units = "cm")Overview of number of samples in different categories (experiment, condition,…).:
#print(key_variables_tableOne$CatTable)
tableone::kableone(key_variables_tableOne$CatTable,
caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
kableExtra::kable_material(c("striped", "hover"))| EXP9.4 | EXP9.5 | EXP9.6 | EXP9.7 | EXP9.8 | p | test | |
|---|---|---|---|---|---|---|---|
| n | 18 | 36 | 39 | 40 | 36 | ||
| condition_tp (%) | <0.001 | ||||||
| Tumor_only_timepoint_0 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Tumor_only_timepoint_1 | 6 (33.3) | 12 (33.3) | 12 (30.8) | 12 (30.0) | 6 (16.7) | ||
| Tumor_only_timepoint_2 | 0 ( 0.0) | 0 ( 0.0) | 4 (10.3) | 4 (10.0) | 6 (16.7) | ||
| Tumor_plus_NK_timepoint_1 | 6 (33.3) | 12 (33.3) | 12 (30.8) | 12 (30.0) | 0 ( 0.0) | ||
| Tumor_plus_NK_timepoint_2 | 6 (33.3) | 12 (33.3) | 11 (28.2) | 12 (30.0) | 0 ( 0.0) | ||
| Tumor_plus_WT_NK_timepoint_2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Tumor_plus_IFNg_timepoint_2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Tumor_plus_PrfKO_NK_timepoint_2 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| condition (%) | <0.001 | ||||||
| Tumor_only | 6 (33.3) | 12 (33.3) | 16 (41.0) | 16 (40.0) | 18 (50.0) | ||
| Tumor_plus_NK | 12 (66.7) | 24 (66.7) | 23 (59.0) | 24 (60.0) | 0 ( 0.0) | ||
| Tumor_plus_IFNg | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Tumor_plus_WT_NK | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Tumor_plus_PrfKO_NK | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| timepoint_cell_harvesting (%) | <0.001 | ||||||
| timepoint_0 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| timepoint_1 | 12 (66.7) | 24 (66.7) | 24 (61.5) | 24 (60.0) | 6 (16.7) | ||
| timepoint_2 | 6 (33.3) | 12 (33.3) | 15 (38.5) | 16 (40.0) | 24 (66.7) | ||
| day_cell_harvesting (%) | <0.001 | ||||||
| Day0 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 6 (16.7) | ||
| Day4 | 12 (66.7) | 24 (66.7) | 24 (61.5) | 0 ( 0.0) | 6 (16.7) | ||
| Day6 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 24 (60.0) | 0 ( 0.0) | ||
| Day10 | 6 (33.3) | 12 (33.3) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | ||
| Day14 | 0 ( 0.0) | 0 ( 0.0) | 15 (38.5) | 0 ( 0.0) | 24 (66.7) | ||
| Day16 | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 16 (40.0) | 0 ( 0.0) | ||
| cell_line_label (%) | <0.001 | ||||||
| A | 0 ( 0.0) | 9 (25.0) | 10 (25.6) | 10 (25.0) | 18 (50.0) | ||
| B | 0 ( 0.0) | 9 (25.0) | 10 (25.6) | 10 (25.0) | 0 ( 0.0) | ||
| C | 9 (50.0) | 9 (25.0) | 10 (25.6) | 10 (25.0) | 0 ( 0.0) | ||
| D | 9 (50.0) | 9 (25.0) | 9 (23.1) | 10 (25.0) | 18 (50.0) | ||
| technical_replicate (%) | 0.998 | ||||||
| 1 | 6 (33.3) | 12 (33.3) | 16 (41.0) | 16 (40.0) | 12 (33.3) | ||
| 2 | 6 (33.3) | 12 (33.3) | 12 (30.8) | 12 (30.0) | 12 (33.3) | ||
| 3 | 6 (33.3) | 12 (33.3) | 11 (28.2) | 12 (30.0) | 12 (33.3) |
Plot of number of samples in each condition across different experiments:
print(cat_cat_plot)
ggsave(filename = paste0(eda_plots_dir, "key_variables_relate_plot.png"),
plot=cat_cat_plot,
width = 20, height = 20, units = "cm")Exploratory Data Analysis - expression
# import::from(.from = SummarizedExperiment, colData)
# import::from(.from = DT, datatable)
# import::from(.from = ggplot2, .all=TRUE)
# import::from(.from = DataExplorer, plot_bar)
# import::from(.from = tableone, CreateTableOne)
# import::from(.from = dplyr, group_by, count)
#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)
# import utils scripts
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", .character_only=TRUE)experiment_name="nk_tum_immunoedit_complete"
output_dir <- paste0(results_dir, "/",experiment_name,"/") # "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/"
# results_dir defined in the nk_immunoediting.Rmd file
eda_plots_dir <- paste0(output_dir, "eda_plots/")
dir.create(eda_plots_dir)
# raw and filtered dds
precomputed_objects_filename <-paste0(experiment_name, "_dds_objects.RData") # "nk_tum_immunoedit_complete_dds_objects.RData"
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# log2 and vsd transformed
precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData") #"nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# check if object loaded if not load
if(!exists(precomputed_objects_file)){
load(precomputed_objects_file)
load(precomputed_transf_objects_file)
}
metadata <- tibble::as_tibble(SummarizedExperiment::colData(dds_filt))
key_metadata <- metadata %>%
dplyr::select(experiment, condition_tp,condition, timepoint_cell_harvesting, day_cell_harvesting, cell_line_label, technical_replicate)
# saving metadata
#openxlsx::write.xlsx(metadata, file = file.path(output_dir, paste0(experiment_name, "_metadata.xlsx")))
# preparing vsd per experiment ----
experiments <- unique(as.character(vsd_filt$experiment))
vsd_filt_perExp <- purrr::map(.x = experiments, .f = function(experiment_name){
vsd_filt[,vsd_filt$experiment == experiment_name]
}) %>% setNames(experiments)
#lapply(vsd_filt_perExp, dim)cond_interest <- "condition_tp" # column of interest for PCA plots etc.
cond_interest_varPart <- c("condition_tp", "condition", "timepoint_cell_harvesting", "cell_line_label", "technical_replicate", "experiment")
cond_interest_varPart_corr <- c("condition_tp", "cell_line_label", "experiment", "technical_replicate")
# #~ (1|technical_replicate) + (1|experiment) + (1|cell_line_label) + (1|condition_tp)
fitform_partVariance <- ~(1 | technical_replicate) + (1 | experiment) + (1 | cell_line_label) + (1 | timepoint_cell_harvesting) + (1 | condition)
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6 # at least 60% variance explained needed
transf_object <- vsd_filt # which object to use for pca, variance partition etc. in case there is e.g. log2norm, rlog,...Variance partition
# calculating overall variance partition ----
variance_partition_all <- varPartitionEstimate(transf_object = transf_object,
fitform_partVariance = fitform_partVariance,
ntop_genes=1000,
ncores = 12)## Selecting 1000 Ntop genes based on variance.# calculating variance partition per experiment ----
fitform_partVariance_perExperiment <- ~(1 | technical_replicate) + (1 | cell_line_label) + (1 | timepoint_cell_harvesting) + (1 | condition)
variance_partition_perExperiment <- purrr::map(.x=names(vsd_filt_perExp), .f=function(experiment_name){
varPartitionEstimate(transf_object = vsd_filt_perExp[[experiment_name]],
fitform_partVariance = fitform_partVariance_perExperiment,
ntop_genes=1000,
ncores = 12)
}) %>% setNames(names(vsd_filt_perExp))## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.
## Selecting 1000 Ntop genes based on variance.#print(key_variables_tableOne$CatTable)
variance_partition_all_plot <- variance_partition_all$varPart_plot_annot + ggtitle("Variance partition all experiments")
print(variance_partition_all_plot)
ggsave(filename = paste0(eda_plots_dir, "variance_partition_all_plot.png"),
plot=variance_partition_all_plot,
width = 20, height = 20, units = "cm")Variance explained by different variables of interest. There seems to be a lot of variation coming from different experiments and cell lines. This will need to be accounted in the design or downstream batch effect removal.
#print(key_variables_tableOne$CatTable)
variance_partition_all$varPart_stats %>%
kableExtra::kbl(caption="Variance partition table - all experiments") %>%
kableExtra::kable_material(c("striped", "hover"))| variable | median_varExplained | mean_varExplained | IQR_varExplained | max_varExplained | min_varExplained |
|---|---|---|---|---|---|
| experiment | 26.5 | 35.9 | 52.3 | 99.7 | 0.0 |
| cell_line_label | 2.8 | 13.4 | 10.4 | 96.6 | 0.0 |
| condition | 0.8 | 7.7 | 8.0 | 82.8 | 0.0 |
| timepoint_cell_harvesting | 0.0 | 1.2 | 0.9 | 33.3 | 0.0 |
| technical_replicate | 0.0 | 0.3 | 0.2 | 6.8 | 0.0 |
| Residuals | 34.1 | 41.5 | 46.0 | 100.0 | 0.3 |
*Please note color coding varies between variance_partition_perExperiment
variance_partition_perExperiment_plots <- purrr::map(.x=names(variance_partition_perExperiment), .f = function(experiment_name){
variance_partition_perExperiment[[experiment_name]]$varPart_plot_annot + ggtitle(paste0("Variance partition experiment - ", experiment_name))
}) %>% setNames(names(variance_partition_perExperiment))
variance_partition_perExperiment_plots <- purrr::map(.x=names(variance_partition_perExperiment), .f = function(experiment_name){
print(variance_partition_perExperiment_plots[[experiment_name]])
ggsave(filename = paste0(eda_plots_dir, paste0("variance_partition_", experiment_name,"_plot.png")),
plot=variance_partition_perExperiment_plots[[experiment_name]],
width = 20, height = 20, units = "cm")
}) %>% setNames(names(variance_partition_perExperiment))
Principal Component Analysis (PCA)
# calculating how many components are needed ----
transf_object_counts_ntop <- rnaSelectTopVarGenes(SummarizedExperiment::assay(transf_object), ntop = 1000, type = "var")## Selecting 1000 Ntop genes based on variance.pca_object <- stats::prcomp(t(transf_object_counts_ntop), center = TRUE)
pca_results <- pcaExtractVariance(pca_object)
pca_scree_cumVarExpl <- pcaPlotVariance(pca_results, var_expl_needed = var_expl_needed)
min_components_needed <- which(pca_results$cummulative_variance > var_expl_needed)[1] # min components to get var_expl_needed
# calculating components correlation with variables ----
#TODO:
# fix - correlation between PCs and variables of interest! - fix sample naming!
# PC_corr <- pcaCorrPCs(pca_object, metadata[cond_interest_varPart_corr], pcs = 1:min_components_needed, pval_exact = TRUE)
# PC_corr_plot <- pcaCorrPCsPlot(PC_corr)
# calculating overall PCA ----
condition_tp_colors <- RColorBrewer::brewer.pal(length(unique(metadata$condition_tp)),"Dark2")
names(condition_tp_colors) <- levels(metadata$condition_tp)
pca_all <- generatePCA(transf_object = transf_object,
cond_interest_varPart = cond_interest_varPart,
color_variable = "condition_tp",
shape_variable = "experiment",
ntop_genes = 1000) +
ggtitle("Overall PCA") +
ggplot2::scale_color_manual(values = condition_tp_colors)
pca_perExperiment_plots <- purrr::map(.x=names(vsd_filt_perExp), .f = function(experiment_name){
pca_plot <- generatePCA(transf_object = vsd_filt_perExp[[experiment_name]],
cond_interest_varPart = cond_interest_varPart,
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000)
pca_plot + ggtitle(experiment_name) +
# make universal color, shape across the whole report!
ggplot2::scale_shape_manual(values = c("A" = 16, "B" = 15, "C"=17, "D"=3)) +
ggplot2::scale_color_manual(values = condition_tp_colors)
}) %>% setNames(names(vsd_filt_perExp))
combined_pca_plot <- ggpubr::ggarrange(pca_perExperiment_plots$EXP9.4,
pca_perExperiment_plots$EXP9.5,
pca_perExperiment_plots$EXP9.6,
pca_perExperiment_plots$EXP9.7,
pca_perExperiment_plots$EXP9.8,
nrow=3, ncol=2,
common.legend = FALSE) #patchwork::wrap_plots(pca_perExperiment_plots, ncol=2)print(pca_scree_cumVarExpl)
ggsave(filename = paste0(eda_plots_dir, "pca_scree_cumVarExpl_plot.png"),
plot=pca_scree_cumVarExpl,
width = 20, height = 20, units = "cm")Overall PCA plot:
print(pca_all)
ggsave(filename = paste0(eda_plots_dir, "pca_all_plot.png"),
plot=pca_all,
width = 20, height = 20, units = "cm")PCA plot per experiment:
print(combined_pca_plot)
ggsave(filename = paste0(eda_plots_dir, "pca_perExperiment_plot.png"),
plot=combined_pca_plot,
width = 20, height = 20, units = "cm")Results - TpNK_tp2_vs_Tonly_tp1
Copied completely from the “Prepare dataset.Rmd”
Trying to keep everythin in one place
The analysis is similar to the previous version, however here I don’t do the correction for the batch correction between AB and CD
# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup) # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = DESeq2, .all=TRUE)
import::from(.from = tximport, tximport)
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filtering#project setup
# loading config file ----
config_file <- file.path( "nk_tum_immunoediting_config.yaml") # params$config_file; params not found? ; change to project_config.yaml
config <- yaml::read_yaml(config_file)
#config <- yaml::read_yaml(params$config_file)
experiment_name="nk_tum_immunoedit_complete" # this can be a subset analysis - e.g. just batch1,...
#experiment_name="nk_tum_immunoedit_complete_ab2_AB_CD_sep" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1" # used only to construct initial dds object
abs_filt_samples=3
# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
#biomart_host = "https://www.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"
# directories
output_dir = paste0("./results_dir", "/",experiment_name,"/")
dir.create(output_dir)Preparing metadata and expression data and combining into a dds (DESeqDataSet object) that is ready for analyses. dds object is further pre-filtered to remove lowly expressed genes (FPM (fragments per million mapped fragments) > 1 in at least 3 samples). Salmon quantification (quant.sf) transcript abundance estimates (transcript expression) is imported and converted to gene expression using tximport.
# Preparing metadata
rnaseq_metadata_file <- here::here(config$metadata$rnaseq$rnaseq_metadata)
rnaseq_metadata_df_raw <- readr::read_csv(file = rnaseq_metadata_file) # 133 samples; 42 samples from other project?
quantseq_files <- here::here(config$metadata$rnaseq$rnaseq_salmon_files)
# salmon quant files
quant_files_md5sums_raw <- readr::read_table(file = quantseq_files, col_names = c("md5sum", "file_path"))
quant_files_md5sums <- quant_files_md5sums_raw %>%
dplyr::mutate(absolute_quant_files_path = gsub(pattern = "\\./", replacement = paste0(dirname(quantseq_files), "/"), file_path),
sample_name = gsub(pattern = "(\\./)(S_.+)(_0_.+)", replacement = "\\2", file_path)) %>%
# for batch3
dplyr::mutate(sample_name = gsub(pattern = "(\\./)(R.+)(_S.+)", replacement = "\\2", sample_name)) %>%
# EK were samples from other experiment - they were removed from quant.sf list, but in case remove them here as well!
dplyr::filter(!grepl(pattern = "\\./EK.+", x = sample_name)) %>%
dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(S_.+)(_quant/quant.sf)", replacement = "\\2", x = file_path)) %>%
# for batch 3
dplyr::mutate(bsf_sample_name = gsub(pattern = "(\\./)(R.+_S.+)(_quant/quant.sf)", replacement = "\\2", x = bsf_sample_name))
# experiment info - match experiment to run
experiment_info_batch1_2_raw <- readr::read_csv("datasets/metadata/rnaseq_merge_sample_sheet.csv", col_types =cols(.default = "c"))
experiment_info_batch3_raw <- readr::read_csv("datasets/metadata/rnaseq_sample_sheet_batch3.csv", col_types = cols(.default = "c"))
experiment_info_raw <- dplyr::bind_rows(experiment_info_batch1_2_raw, experiment_info_batch3_raw)
# EK samples are from different experiment
experiment_info <- experiment_info_raw %>%
dplyr::filter(!grepl(pattern = "EK.+", x = original_sample_name)) %>%
dplyr::rename(library_name = experiment,
bsf_sample_name = sample_name) %>%
# fixing library_name for experiment EXP9.8
dplyr::mutate(library_name = dplyr::if_else(library_name == "BSF_1241", "R0128_L5464", library_name)) %>%
dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=original_sample_name) ~ paste0(original_sample_name, "_", library_name),
TRUE ~ paste0("S_", original_sample_name, "_", library_name)))
# loading metadata and adding experiment and salmon data
rnaseq_metadata_df <- rnaseq_metadata_df_raw %>%
dplyr::mutate(condition_complete = condition) %>%
dplyr::mutate(condition = gsub(pattern = "(.+)(_Day.+)", replacement = "\\1", condition_complete)) %>%
dplyr::mutate(condition = gsub(pattern = "\\+", replacement = "_plus_", condition),
timepoint_cell_harvesting = gsub(pattern = " ", replacement = "_", timepoint_cell_harvesting),
experiment = gsub(pattern = " ", replacement = "", original_experiment)) %>%
dplyr::mutate(sample_name_unique = dplyr::case_when(grepl(pattern = "^R", x=sample_name) ~ paste0(sample_name, "_", library_name),
TRUE ~ paste0("S_", sample_name, "_", library_name)))
quant_files_md5sums_annot <- quant_files_md5sums %>%
dplyr::left_join(., experiment_info, by = "bsf_sample_name") %>%
dplyr::select(-sample_name, -library_name)
rnaseq_metadata_df_annot <- rnaseq_metadata_df %>%
dplyr::left_join(., quant_files_md5sums_annot, by="sample_name_unique") %>%
dplyr::mutate(original_sample_name = sample_name) %>%
dplyr::mutate(sample_name = sample_name_unique) %>%
dplyr::mutate(filenames = sample_name_unique)
#table(rnaseq_metadata_df_annot$original_experiment)
#table(rnaseq_metadata_df_annot$cell_line_label)
rnaseq_metadata_df_annot_reduced <- rnaseq_metadata_df_annot %>%
#dplyr::filter(original_experiment == "EXP9_6") %>%
#dplyr::filter(condition %in% c("Tumor_only", "Tumor_plus_NK_Day14")) %>%
dplyr::mutate(technical_replicate = factor(technical_replicate, levels = c("1", "2", "3")),
condition = factor(condition, levels = c("Tumor_only", "Tumor_plus_NK", "Tumor_plus_IFNg", "Tumor_plus_WT_NK", "Tumor_plus_PrfKO_NK")),
timepoint_cell_harvesting = factor(timepoint_cell_harvesting, levels = c("timepoint_0", "timepoint_1", "timepoint_2")),
cell_line_label = as.factor(cell_line_label),
experiment = factor(experiment, levels = c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7", "EXP9.8")),
condition_tp = paste0(condition,"_",timepoint_cell_harvesting)) %>%
dplyr::mutate(condition_tp = factor(condition_tp, levels = c("Tumor_only_timepoint_0",
"Tumor_only_timepoint_1", "Tumor_only_timepoint_2",
"Tumor_plus_NK_timepoint_1", "Tumor_plus_NK_timepoint_2",
"Tumor_plus_WT_NK_timepoint_2",
"Tumor_plus_IFNg_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"))) %>%
#fixing day_cell_harvesting
dplyr::mutate(day_cell_harvesting = gsub(pattern = " ", replacement = "", day_cell_harvesting)) %>%
dplyr::mutate(day_cell_harvesting = factor(day_cell_harvesting, levels=c("Day0", "Day4", "Day6", "Day10", "Day14", "Day16")))
metadata <- rnaseq_metadata_df_annot_reduced #cond_data
#rm(reseq_metadata)
rownames(metadata) <- metadata$filenames
# use alternative sample names for multiQC
# renaming for the multiQC report
# metadata_multiqc <- metadata %>%
# dplyr::select(bsf_sample_name, experiment, technical_replicate, cell_line_label, condition, timepoint_cell_harvesting) %>%
# dplyr::mutate(cell_line_label = paste0("cl", cell_line_label),
# condition = gsub(pattern = "Tumor", replacement = "T", x = condition),
# timepoint_cell_harvesting = gsub(pattern = "timepoint", replacement = "tp", x=timepoint_cell_harvesting),
# technical_replicate = paste0("repl",technical_replicate)) %>%
# dplyr::mutate(alt_name = paste(experiment, cell_line_label, technical_replicate, condition, timepoint_cell_harvesting, sep="-")) %>%
# dplyr::select(bsf_sample_name, alt_name)
#
# readr::write_tsv(metadata_multiqc, file = paste0(OUTPUT_DIR, experiment_name, "_metadata_multiqc.tsv"))#TODO:
# [ ] - add md5sums for tx2gene_file
data_dir <- file.path("datasets")
annotation_dir = paste0(data_dir, "/annotations/")
gtf_file <- file.path(annotation_dir, "Mus_musculus.GRCm38.102.gtf") # needed only once to generate tx2gene.tsv
tx2gene_file <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.tsv")
tx2gene_file_md5sum <- file.path(annotation_dir, "EnsDb.Mmusculus.v102_tx2gene.md5sum")
if (!file.exists(tx2gene_file)) {
message(tx2gene_file, " does not exist: generating!")
txDB <- GenomicFeatures::makeTxDbFromGFF(gtf_file, format="gtf")
# AnnotationDbi::keys
tx_name <- annot_db_keys(txDB, keytype="TXNAME")
# AnnotationDbi::select
tx2gene_gtf <- annot_db_select(txDB,
keys = tx_name,
columns="GENEID",
keytype = "TXNAME")
# saving tx2gene into destdir. Potentially there is a typo here. The name should be tx2gene, not _gtf
write.table(tx2gene_gtf,
file = tx2gene_file,
sep = "\t",
col.names = FALSE,
row.names = FALSE,
quote = FALSE)
tx2gene_md5sum <- digest::digest(tx2gene_file, file=TRUE, algo="md5")
write.table(paste(tx2gene_md5sum, basename(tx2gene_file), collapse = "\t"),
file = tx2gene_file_md5sum,
sep = "\t",
col.names = FALSE,
row.names = FALSE,
quote = FALSE)
} else {
message(tx2gene_file, " exists: loading!")
tx2gene <- readr::read_tsv(tx2gene_file,
col_names = c("tx_id", "gene_id"),
show_col_types = FALSE)
}precomputed_objects_filename <- paste0("nk_tum_immunoedit_complete", "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
precomputed_transf_objects_filename <- paste0("nk_tum_immunoedit_complete", "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
if(!file.exists(precomputed_objects_file)){
cat("RNA objects file '", precomputed_objects_filename, "' does not exist in the OUTPUT_DIR...\n")
cat("generating", precomputed_objects_filename, "\n")
# Generating dds and filtered dds objects ----
#require(DESeq2)
# here there is a single sample so we use ~1.
# expect a warning that there is only a single sample...
# generating dds object ----
# txi_object <- generateTxi(metadata_object=metadata,
# column_names=c("absolute_quant_files_path", "filenames"),
# tx2gene = tx2gene)
quant_files <- metadata$absolute_quant_files_path
names(quant_files) <- metadata$sample_name
txi_object <- tximport::tximport(quant_files,
type="salmon",
tx2gene = tx2gene,
ignoreTxVersion = TRUE)
sum(rownames(metadata) == colnames(txi_object$counts)) # check if names match between metadata and counts data
cat("... txi_object\n")
dds <- DESeq2::DESeqDataSetFromTximport(txi_object,
colData = metadata,
design = as.formula(paste0("~", experiment_design)))
cat("... dds\n")
dds <- DESeq2::estimateSizeFactors(dds) # Isn't the data already normalized by Salmon?
dds <- DESeq2::DESeq(dds, minReplicatesForReplace=Inf) # do not replace outliers based on replicates
# filtering dds
cat("... dds filtering\n")
dds_filt <- filterDatasets(dds, abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
dds_filt <- DESeq2::estimateSizeFactors(dds_filt)
# Generate annotation file using biomaRt
#FIXME:
# Issues with Curl when using https:// and http:// throws a warning:
# Warning: Ensembl will soon enforce the use of https.
# Ensure the 'host' argument includes "https://"
cat("... fetching annotation from biomart\n")
ensemblAnnot <- generateEnsemblAnnotation(ensembl_ids = rownames(dds),
host=biomart_host,
version=biomart_Ens_version,
dataset=biomart_dataset)
cat("saving...", precomputed_objects_file, "\n")
cat("...into:", output_dir, "\n")
save(dds, dds_filt, ensemblAnnot, file = precomputed_objects_file)
} else{
cat("RNA objects file '", precomputed_objects_filename, "' exist...loading\n")
load(precomputed_objects_file)
}## RNA objects file ' nk_tum_immunoedit_complete_dds_objects.RData ' exist...loading#transformation after filtering
if(!file.exists(precomputed_transf_objects_file)){
# transformation
log2_norm_filt <- DESeq2::normTransform(dds_filt)
vsd_filt <- DESeq2::vst(dds_filt, blind = TRUE) # blind = TRUE for QC
#rld_filt <- rlog(dds_filt, blind = TRUE)
#rld_filt - too big for >100 samples; skipping!
#rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
save(log2_norm_filt, vsd_filt,
file = precomputed_transf_objects_file)
} else{
load(precomputed_transf_objects_file)
}Parameters for dataset preparation:
| Parameter | Value | Decription |
|---|---|---|
| experiment_name | nk_tum_immunoedit_complete | this can be a subset analysis - e.g. just batch1,… |
| experiment_design | 1 | used only to construct initial dds object |
| abs_filt_samples | 3 | # at least in N samples, which is a smallest group size |
| biomart_host | http://nov2020.archive.ensembl.org | |
| biomart_Ens_version | Ensembl Genes 102 | |
| biomart_dataset | mmusculus_gene_ensembl | |
Objects overview:
dds_objects_overview <- data.frame(dds_object = c("dds", "dds_filt"),
n_samples = c(ncol(dds), ncol(dds_filt)),
n_genes = c(nrow(dds), nrow(dds_filt)))
dds_objects_overview %>%
kableExtra::kbl(caption = "dds objects overview") %>%
kableExtra::kable_classic(full_width = T)| dds_object | n_samples | n_genes |
|---|---|---|
| dds | 169 | 51149 |
| dds_filt | 169 | 15430 |
# DT::datatable(dds_objects_overview,
# options = list(pageLength = 10),
# caption = 'Table 1: Samples in validation set')End of the data_prep steps
#import::from(.from = SummarizedExperiment, colData, assay)
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)
# import utils scripts
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", "generatePCA_plus_shape", .character_only=TRUE)
import::from(.from = here::here("utils/generateResults.R"), "generateResults_upd", .character_only=TRUE) #
import::from(.from = here::here("utils/plotDegResults.R"), "plotVolcano","plotVolcano_repel", .character_only=TRUE)
library(ggrepel)Differential expression analysis
Comparison between Tumor_plus_NK vs Tumor_only - specifically Tumor_plus_NK_timepoint_2 against Tumor_only_timepoint_1. Tumor_plus_WT_NK_timepoint_2 was renamed to Tumor_plus_NK_timepoint_2
Steps:
- subsetting dds_filt to contain only condition_tp of interest (Tumor_only_timepoint_1, Tumor_plus_NK_timepoint_2 = Tumor_plus_WT_NK_timepoint_2)
- filter (again) for lowly expressed genes
- results with differentially expressed genes (DEG) and significantly DEG at a selected cutoff
- enrichment analysis (over-representation analysis) of the significantly DEG genes; GOBP, GOCC and GOMF terms enrichment
abs_filt_samples=3
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6 # at least 60% variance explained needed
# compare Tumor_only between timepoints!
# condition_tp_subset <- c("Tumor_only_timepoint_0",
# "Tumor_only_timepoint_1",
# "Tumor_only_timepoint_2",
# "Tumor_plus_NK_timepoint_1",
# "Tumor_plus_NK_timepoint_2",
# "Tumor_plus_WT_NK_timepoint_2")
condition_tp_subset <- c("Tumor_only_timepoint_1",
"Tumor_plus_NK_timepoint_2",
"Tumor_plus_NK_timepoint_1",
"Tumor_plus_WT_NK_timepoint_2")
genes_of_interest <- list("Denn2b"="ENSMUSG00000031024",
"Gbp6"="ENSMUSG00000104713",
"H2-K1"="ENSMUSG00000061232",
"Hs3st2"="ENSMUSG00000046321",
"Htra3"="ENSMUSG00000029096",
"Ifi27"="ENSMUSG00000064215",
"Igtp"="ENSMUSG00000078853",
"Irf1"="ENSMUSG00000018899",
"Lgals3bp"="ENSMUSG00000033880",
"Ly6a"="ENSMUSG00000075602",
"Plaat3"="ENSMUSG00000060675",
"Rtp4"="ENSMUSG00000033355",
"Serpina3f"="ENSMUSG00000066363",
"Stat1"="ENSMUSG00000026104")
genes_of_interest_exp9.4_9.7 <- list(
'Ifi44' ="ENSMUSG00000028037",
'Ccl5' ="ENSMUSG00000035042",
'Iigp1' ="ENSMUSG00000054072",
'Serpina3f' ="ENSMUSG00000066363",
'Ly6a' ="ENSMUSG00000075602",
'Plaat3' ="ENSMUSG00000060675",
'Lgals3bp' ="ENSMUSG00000033880",
'Ifi27' ="ENSMUSG00000064215",
'Gbp6' ="ENSMUSG00000104713",
'Rtp4' ="ENSMUSG00000033355",
'Stat1' ="ENSMUSG00000026104",
'Tap1' ="ENSMUSG00000037321",
'H2-K1' ="ENSMUSG00000061232",
'B2m' ="ENSMUSG00000060802",
'Igtp' ="ENSMUSG00000078853",
'Gtf2ird1' ="ENSMUSG00000023079",
'Cdc42ep4' ="ENSMUSG00000041598",
'Hoxb6' ="ENSMUSG00000000690",
'Bfsp2' ="ENSMUSG00000032556",
'Sox6' ="ENSMUSG00000051910",
'Tgm2' ="ENSMUSG00000037820",
'Adprhl1' ="ENSMUSG00000031448")
# additional parameters ----
if(exists("deg_design")) {rm(deg_design)}
deg_design <- as.formula("~ experiment + cell_line_label + condition_tp")
# # raw and filtered dds
# precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
# precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
#
# # log2 and vsd transformed
# precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
# precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
#
# # check if object loaded if not load
# if(!exists(precomputed_objects_file)){
# load(precomputed_objects_file)
# load(precomputed_transf_objects_file)
# } # preparing dds and vsd objects for the main project comparison
# We use only EXP 9.4 - 9.7. EXP 9.8 is excluded.
# Preparing AB subset.
# defining parameters
deg_name <- "TpNK_tp2_vs_Tonly_tp1_AB"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")
# just in case remove any existing subsets
if(exists("dds_subset")) {rm(dds_subset)}
if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
if(exists("vsd_subset")) {rm(vsd_subset)}
if(!file.exists(precomputed_objects_deg_file)){
cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
cat("generating", precomputed_objects_deg_filename, "\n")
dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]
dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
dds_subset <- dds_subset[ , dds_subset$cell_line_label %in% c("A", "B")]
# fixing Tumor_plus_WT_NK_timepoint_2 -> Tumor_plus_NK_timepoint_2
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)
dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
table(dds_subset$cell_line_label)
# filtering lowly expressed genes
dds_subset_filt <- filterDatasets(dds_subset,
abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
cat("... dds\n")
design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
cat("saving...", precomputed_objects_file, "\n")
cat("...into:", output_dir,deg_name,"\n")
save(dds_subset, dds_subset_filt, ensemblAnnot, file = precomputed_objects_deg_file)
} else{
cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
load(precomputed_objects_deg_file)
}## RNA objects file ' TpNK_tp2_vs_Tonly_tp1_AB_dds_objects.RData ' exist...loading#transformation after filtering
if(!file.exists(precomputed_transf_objects_deg_file)){
# transformation
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
#rld_filt <- DESeq2::rlog(dds_subset_filt, blind = FALSE) # more robust to differences in sequencing depth!
#rld_filt - too big for >100 samples; skipping!
#rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
save(log2_norm_subset_filt, vsd_subset_filt,
file = precomputed_transf_objects_deg_file)
} else{
load(precomputed_transf_objects_deg_file)
}#get the counts table for diffTFs AB cell lines
fl_smpl_nm <- gsub("_Day\\d\\d","",dds_subset@colData@listData[["full_sample_name"]])
fl_smpl_nm <- gsub("_\\d_","_",fl_smpl_nm)
sample_name <- paste0(dds_subset@colData@listData[["original_experiment"]], "_",
fl_smpl_nm, "_",
dds_subset@colData@listData[["day_cell_harvesting"]], "_REP",
dds_subset@colData@listData[["technical_replicate"]])
sample_name <- gsub("^EXP\\s9.", "EXP9_", sample_name)
sample_name <- gsub("\\+", "_plus_", sample_name)
sample_name <- gsub("_Tumor_only_Day\\d_", "_Tumor_only_", sample_name)
#These samples from RNA-seq are missing in ATAC data.
#EXP9_6_13G_Tumor_plus_NK_Day14_REP1 is missing in ./
#EXP9_6_13G_Tumor_plus_NK_Day14_REP2 is missing in ./
#EXP9_6_13G_Tumor_plus_NK_Day14_REP3 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP1 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP2 is missing in ./
#EXP9_6_13H_Tumor_plus_NK_Day14_REP3 is missing in ./
#
#Instead there is Day_10 version.
#
#I'm going to replace in the RNA-seq count table the day 14 with day 10, so that samples match.
sample_name <- gsub("EXP9_6_13G_Tumor_plus_NK_Day14", "EXP9_6_13G_Tumor_plus_NK_Day10", sample_name)
sample_name <- gsub("EXP9_6_13H_Tumor_plus_NK_Day14", "EXP9_6_13H_Tumor_plus_NK_Day10", sample_name)
col_IDS <- c("ENSEMBL", sample_name)
RNA_seq_raw_counts <- dds_subset@assays@data@listData[["counts"]]
RNA_seq_raw_counts <- cbind(row.names(RNA_seq_raw_counts), RNA_seq_raw_counts)
RNA_seq_raw_counts <- rbind(col_IDS,RNA_seq_raw_counts)
write.table(RNA_seq_raw_counts, file = "~/workspace/RNA_seq_raw_counts_AB.tsv",
sep ="\t", quote = FALSE, row.names = FALSE, col.names = FALSE)
# Building the sample table
sampleID_list <- list("sampleID" = sample_name)
bamReads_list <- list("bamReads" = paste0("/home/aleksandr_b/bioinf_isilon/core_bioinformatics_unit/Internal/aleksandr/projects/ab3_20230220_nk_tumor_project/data_ATACSeq_nfcore_run/results/bwa/merged_library/", sample_name, ".mLb.clN.sorted.bam"))
cond_summ <- sample_name
cond_summ[stringr::str_detect(cond_summ, pattern = "_Tumor_only_")] <- "Tumor_only_TP_1"
cond_summ[stringr::str_detect(cond_summ, pattern = "_Tumor_plus_NK")] <- "Tumor_pls_NK_TP2"
conditionSummary_list <- list("conditionSummary" = cond_summ)
experiment_id_list <- list("experiment_id" = stringr::str_extract(sample_name, "EXP\\d_\\d"))
sampleData.tsv <- data.frame(sampleID_list,
bamReads_list,
conditionSummary_list,
experiment_id_list)
write.table(sampleData.tsv, file = "~/workspace/sampleData.tsv",
sep ="\t", quote = FALSE, row.names = FALSE)#print(key_variables_tableOne$CatTable)
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previus runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate)
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)),
strata = c("condition_tp"),
data = key_metadata_subset)
tableone::kableone(key_variables_tableOne$CatTable,
caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
kableExtra::kable_material(c("striped", "hover"))| Tumor_only_timepoint_1 | Tumor_plus_NK_timepoint_1 | Tumor_plus_NK_timepoint_2 | p | test | |
|---|---|---|---|---|---|
| n | 18 | 18 | 18 | ||
| experiment (%) | 1.000 | ||||
| EXP9.5 | 6 (33.3) | 6 (33.3) | 6 (33.3) | ||
| EXP9.6 | 6 (33.3) | 6 (33.3) | 6 (33.3) | ||
| EXP9.7 | 6 (33.3) | 6 (33.3) | 6 (33.3) | ||
| cell_line_label = B (%) | 9 (50.0) | 9 (50.0) | 9 (50.0) | 1.000 | |
| technical_replicate (%) | 1.000 | ||||
| 1 | 6 (33.3) | 6 (33.3) | 6 (33.3) | ||
| 2 | 6 (33.3) | 6 (33.3) | 6 (33.3) | ||
| 3 | 6 (33.3) | 6 (33.3) | 6 (33.3) |
#TODO:
# - plot images after removing batch effects!
# EDA and visualize plots before and after batch effect correction
# transf_object <- vsd_subset_filt
# pca_deg <- generatePCA(transf_object = transf_object,
# cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
# color_variable = "condition_tp",
# shape_variable = "experiment",
# ntop_genes = 1000) +
# ggtitle("Original dataset") +
# geom_text_repel(aes(label = transf_object$sample_name),
# box.padding = 3)
#
#
#
# pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object,
# cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
# color_variable = "condition_tp",
# shape_variable = "cell_line_label",
# ntop_genes = 1000) +
# ggtitle("Original dataset")
#
# # Remove batch effect
# transf_batch_NObatch_experiment <- vsd_subset_filt
# transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
# SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
#
# pca_deg_NObatch_experiment <- generatePCA(transf_object = transf_batch_NObatch_experiment,
# cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
# color_variable = "condition_tp",
# shape_variable = "experiment",
# ntop_genes = 1000) +
# ggtitle("Removed batchEffect - experiment")
#
# pca_deg_NObatch_experiment_plotCellLabel <- generatePCA(transf_object = transf_batch_NObatch_experiment,
# cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
# color_variable = "condition_tp",
# shape_variable = "cell_line_label",
# ntop_genes = 1000) +
# ggtitle("Removed batchEffect - experiment")
#
# #PCA_batch_merged <- ggpubr::ggarrange(pca_deg, pca_deg_NObatch_experiment, common.legend = TRUE)
#
#
# PCA_batch_merged <- ggpubr::ggarrange(pca_deg,
# pca_deg_NObatch_experiment,
# common.legend = TRUE)
#
# PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel,
# pca_deg_NObatch_experiment_plotCellLabel,
# common.legend = TRUE)
###### Plots for publication #######
transf_object <- vsd_subset_filt
pca_deg <- generatePCA_plus_shape(transf_object = transf_object,
cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"),
color_variable = "condition_tp",
shape_variable = "Cell_line_label_and_exp_id",
ntop_genes = 1000)+
ggtitle("Original dataset") +
scale_shape_manual(values = c(0,1,2,15,16,17)) +
scale_color_manual(values = c("#DD3344", "#FF9F1C" ,"#553388"))
pca_deg
# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
pca_deg_NObatch_experiment <- generatePCA_plus_shape(transf_object = transf_batch_NObatch_experiment,
cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"),
color_variable = "condition_tp",
shape_variable = "Cell_line_label_and_exp_id",
ntop_genes = 1000)+
ggtitle("Batch Corrected Dataset") +
scale_shape_manual(values = c(0,1,2,15,16,17)) +
scale_color_manual(values = c("#DD3344", "#FF9F1C" ,"#553388"))
pca_deg_NObatch_experiment
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_not_batch_corrected.png"),
plot=pca_deg,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_batch_corrected.png"),
plot=pca_deg_NObatch_experiment,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_not_batch_corrected.eps"),
plot=pca_deg,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_PCA_batch_corrected.eps"),
plot=pca_deg_NObatch_experiment,
width = 20, height = 14, units = "cm")The following PCA plots show effect of removing batch effects (experiment). There are two weird samples from EXP9.7 that create a lot of variance. What should i do with them?
print(pca_deg_NObatch_experiment)
print(pca_deg)
#resultsNames(dds_subset_filt)
# timepoint2 (T+NK)_vs_timepoint1 (T)
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt,
coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
cond_numerator = "Tumor_plus_NK_timepoint_2",
cond_denominator = "Tumor_only_timepoint_1",
cond_variable="condition_tp")
openxlsx::write.xlsx(deg_TpNK_tp2_vs_Tonly_tp1_results, file = file.path(deg_dir, "deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx"))
AB_results <- deg_TpNK_tp2_vs_Tonly_tp1_results# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE)
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE)
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE)
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE)
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE)
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOBP,
test="hypergeometric",
background=nrow(dds_subset_filt))
AB_GOBP <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOCC,
test="hypergeometric",
background=nrow(dds_subset_filt))
AB_GOCC <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOMF,
test="hypergeometric",
background=nrow(dds_subset_filt))
AB_GOMF <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF
#hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOBP.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOCC.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_enrichment_C5_GOMF.xlsx"))
# plotting enrichment results ----
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOBP: T+NK timepoint2 vs T-only timepoint1 AB") +theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOCC: T+NK timepoint2 vs T-only timepoint1 AB") +theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOMF:T+NK timepoint2 vs T-only timepoint1 AB") + theme_bw()
###### For publication ######
ggsave(filename = paste0(deg_dir,"publication/" ,"TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/" ,"TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.eps"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")Highlighting genes of interest
Volcano plot highlighting genes of interest:
# volcano plot
# heatmap - corrected
genes_of_interest## $Denn2b
## [1] "ENSMUSG00000031024"
##
## $Gbp6
## [1] "ENSMUSG00000104713"
##
## $`H2-K1`
## [1] "ENSMUSG00000061232"
##
## $Hs3st2
## [1] "ENSMUSG00000046321"
##
## $Htra3
## [1] "ENSMUSG00000029096"
##
## $Ifi27
## [1] "ENSMUSG00000064215"
##
## $Igtp
## [1] "ENSMUSG00000078853"
##
## $Irf1
## [1] "ENSMUSG00000018899"
##
## $Lgals3bp
## [1] "ENSMUSG00000033880"
##
## $Ly6a
## [1] "ENSMUSG00000075602"
##
## $Plaat3
## [1] "ENSMUSG00000060675"
##
## $Rtp4
## [1] "ENSMUSG00000033355"
##
## $Serpina3f
## [1] "ENSMUSG00000066363"
##
## $Stat1
## [1] "ENSMUSG00000026104"genes_of_interest_exp9.4_9.7## $Ifi44
## [1] "ENSMUSG00000028037"
##
## $Ccl5
## [1] "ENSMUSG00000035042"
##
## $Iigp1
## [1] "ENSMUSG00000054072"
##
## $Serpina3f
## [1] "ENSMUSG00000066363"
##
## $Ly6a
## [1] "ENSMUSG00000075602"
##
## $Plaat3
## [1] "ENSMUSG00000060675"
##
## $Lgals3bp
## [1] "ENSMUSG00000033880"
##
## $Ifi27
## [1] "ENSMUSG00000064215"
##
## $Gbp6
## [1] "ENSMUSG00000104713"
##
## $Rtp4
## [1] "ENSMUSG00000033355"
##
## $Stat1
## [1] "ENSMUSG00000026104"
##
## $Tap1
## [1] "ENSMUSG00000037321"
##
## $`H2-K1`
## [1] "ENSMUSG00000061232"
##
## $B2m
## [1] "ENSMUSG00000060802"
##
## $Igtp
## [1] "ENSMUSG00000078853"
##
## $Gtf2ird1
## [1] "ENSMUSG00000023079"
##
## $Cdc42ep4
## [1] "ENSMUSG00000041598"
##
## $Hoxb6
## [1] "ENSMUSG00000000690"
##
## $Bfsp2
## [1] "ENSMUSG00000032556"
##
## $Sox6
## [1] "ENSMUSG00000051910"
##
## $Tgm2
## [1] "ENSMUSG00000037820"
##
## $Adprhl1
## [1] "ENSMUSG00000031448"TpNK_tp2_vs_Tonly_tp1_volcano_plot_1 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest),
plot_title = "T+NK tp2 vs T-only tp1 AB genes set 1")
TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest_exp9.4_9.7),
plot_title = "T+NK tp2 vs T-only tp1 AB genes set 2")
##### For publication #####
TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano_repel(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest_exp9.4_9.7),
plot_title = "T+NK tp2 vs T-only tp1 AB")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_volcano_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
width = 18, height = 12, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_AB_volcano_plot.eps"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
width = 18, height = 12, units = "cm")print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_1)
print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_2)
Heatmap of normalized and scaled expression of 607 significantly differentially expressed genes before and after removeal of batch effects coming from different experiments:
# heatmap and heatmap batch corrected - corrected
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)
heatmap_not_corrected <- pheatmap::pheatmap(heatmap_counts_deg_ord,
main = "Heatmap of signif. DEG",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # 
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_not_corrected.png"),
plot=heatmap_not_corrected,
width = 20, height = 20, units = "cm")# heatmap and heatmap batch corrected - corrected
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - Batch Corrected",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # 
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.png"),
plot=heatmap_corrected,
width = 20, height = 20, units = "cm")
##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.eps"),
plot=heatmap_corrected,
width = 18, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected.png"),
plot=heatmap_corrected,
width = 18, height = 20, units = "cm")# identify the 25 gene set
minL2FC_Ids <- order(deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
maxL2FC_Ids <- order(decreasing = T, deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
L2FC_EMSEMBL <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id[c(minL2FC_Ids, maxL2FC_Ids)]
L2FC_EMSEMBL <- c(L2FC_EMSEMBL, "ENSMUSG00000075602", "ENSMUSG00000060675")
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% L2FC_EMSEMBL, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
rownames(heatmap_counts_NObatch_deg_ord) <- ensembl2symbol_annot[match(x = row.names(heatmap_counts_NObatch_deg_ord), table = ensembl2symbol_annot$ensembl_id),]$mgi_symbol
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - Batch Corrected 50 most regulated genes",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = T,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # <img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB”small heatmap”-1.png” width=“672” />
##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected_50genes.eps"),
plot=heatmap_corrected,
width = 24, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_heatmap_corrected_50_genes.png"),
plot=heatmap_corrected,
width = 24, height = 20, units = "cm")Now - process the CD subset. Later I will check the overlap between these two.
# preparing dds and vsd objects for the main project comparison
# We use only EXP 9.4 - 9.7. EXP 9.8 is excluded.
# Preparing CD subset.
# defining parameters
deg_name <- "TpNK_tp2_vs_Tonly_tp1_CD"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")
# just in case remove any existing subsets
if(exists("dds_subset")) {rm(dds_subset)}
if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
if(exists("vsd_subset")) {rm(vsd_subset)}
if(!file.exists(precomputed_objects_deg_file)){
cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
cat("generating", precomputed_objects_deg_filename, "\n")
dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]
dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
dds_subset <- dds_subset[ , dds_subset$cell_line_label %in% c("C", "D")]
# fixing Tumor_plus_WT_NK_timepoint_2 -> Tumor_plus_NK_timepoint_2
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)
dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
table(dds_subset$cell_line_label)
# filtering lowly expressed genes
dds_subset_filt <- filterDatasets(dds_subset,
abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
cat("... dds\n")
design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
cat("saving...", precomputed_objects_file, "\n")
cat("...into:", output_dir, "\n")
save(dds_subset, dds_subset_filt, ensemblAnnot, file = precomputed_objects_deg_file)
} else{
cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
load(precomputed_objects_deg_file)
}## RNA objects file ' TpNK_tp2_vs_Tonly_tp1_CD_dds_objects.RData ' exist...loading#transformation after filtering
if(!file.exists(precomputed_transf_objects_deg_file)){
# transformation
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
#rld_filt <- DESeq2::rlog(dds_subset_filt, blind = FALSE) # more robust to differences in sequencing depth!
#rld_filt - too big for >100 samples; skipping!
#rld_filt <- rlog(dds_filt, blind = FALSE) # not blind to batch effects
save(log2_norm_subset_filt, vsd_subset_filt,
file = precomputed_transf_objects_deg_file)
} else{
load(precomputed_transf_objects_deg_file)
}#print(key_variables_tableOne$CatTable)
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previus runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate)
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)),
strata = c("condition_tp"),
data = key_metadata_subset)
tableone::kableone(key_variables_tableOne$CatTable,
caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
kableExtra::kable_material(c("striped", "hover"))| Tumor_only_timepoint_1 | Tumor_plus_NK_timepoint_1 | Tumor_plus_NK_timepoint_2 | p | test | |
|---|---|---|---|---|---|
| n | 24 | 24 | 23 | ||
| experiment (%) | 1.000 | ||||
| EXP9.4 | 6 (25.0) | 6 (25.0) | 6 (26.1) | ||
| EXP9.5 | 6 (25.0) | 6 (25.0) | 6 (26.1) | ||
| EXP9.6 | 6 (25.0) | 6 (25.0) | 5 (21.7) | ||
| EXP9.7 | 6 (25.0) | 6 (25.0) | 6 (26.1) | ||
| cell_line_label = D (%) | 12 (50.0) | 12 (50.0) | 11 (47.8) | 0.985 | |
| technical_replicate (%) | 1.000 | ||||
| 1 | 8 (33.3) | 8 (33.3) | 8 (34.8) | ||
| 2 | 8 (33.3) | 8 (33.3) | 8 (34.8) | ||
| 3 | 8 (33.3) | 8 (33.3) | 7 (30.4) |
#TODO:
# - plot images after removing batch effects!
# EDA and visualize plots before and after batch effect correction
transf_object <- vsd_subset_filt
pca_deg <- generatePCA(transf_object = transf_object,
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "experiment",
ntop_genes = 1000) +
ggtitle("Original dataset")
pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object,
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle("Original dataset")
# Remove batch effect
# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
pca_deg_NObatch_experiment <- generatePCA(transf_object = transf_batch_NObatch_experiment,
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "experiment",
ntop_genes = 1000) +
ggtitle("Removed batchEffect - experiment")
pca_deg_NObatch_experiment_plotCellLabel <- generatePCA(transf_object = transf_batch_NObatch_experiment,
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle("Removed batchEffect - experiment")
#PCA_batch_merged <- ggpubr::ggarrange(pca_deg, pca_deg_NObatch_experiment, common.legend = TRUE)
PCA_batch_merged <- ggpubr::ggarrange(pca_deg,
pca_deg_NObatch_experiment,
common.legend = TRUE)
PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel,
pca_deg_NObatch_experiment_plotCellLabel,
common.legend = TRUE)
#### for publication #####
transf_object <- vsd_subset_filt
pca_deg <- generatePCA_plus_shape(transf_object = transf_object,
cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"),
color_variable = "condition_tp",
shape_variable = "Cell_line_label_and_exp_id",
ntop_genes = 1000)+
ggtitle("Original dataset") +
scale_shape_manual(values = c(0,1,2,5,15,16,17,18)) +
scale_color_manual(values = c("#DD3344", "#FF9F1C", "#553388"))
pca_deg
# remove batch effect
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
pca_deg_NObatch_experiment <- generatePCA_plus_shape(transf_object = transf_batch_NObatch_experiment,
cond_interest_varPart = c("cell_line_label", "experiment","condition_tp"),
color_variable = "condition_tp",
shape_variable = "Cell_line_label_and_exp_id",
ntop_genes = 1000)+
ggtitle("Batch Corrected Dataset") +
scale_shape_manual(values = c(0,1,2,5,15,16,17,18)) +
scale_color_manual(values = c("#DD3344", "#FF9F1C", "#553388"))
pca_deg_NObatch_experiment
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_not_batch_corrected.png"),
plot=pca_deg,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_batch_corrected.png"),
plot=pca_deg_NObatch_experiment,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_not_batch_corrected.eps"),
plot=pca_deg,
width = 20, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_PCA_batch_corrected.eps"),
plot=pca_deg_NObatch_experiment,
width = 20, height = 14, units = "cm")#resultsNames(dds_subset_filt)
# timepoint2 (T+NK)_vs_timepoint1 (T)
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt,
coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
cond_numerator = "Tumor_plus_NK_timepoint_2",
cond_denominator = "Tumor_only_timepoint_1",
cond_variable="condition_tp")
CD_results <- deg_TpNK_tp2_vs_Tonly_tp1_results
openxlsx::write.xlsx(deg_TpNK_tp2_vs_Tonly_tp1_results, file = file.path(deg_dir, "deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx"))#msigdbr::msigdbr_species()
#packageVersion("msigdbr")
#msigdbr::msigdbr_collections()
# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE)
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE)
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE)
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE)
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE)
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOBP,
test="hypergeometric",
background=nrow(dds_subset_filt))
CD_GOBP <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOCC,
test="hypergeometric",
background=nrow(dds_subset_filt))
CD_GOCC <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$mgi_symbol,
genesets = gs_C5_GOMF,
test="hypergeometric",
background=nrow(dds_subset_filt))
CD_GOMF <- TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF
#hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOBP.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOCC.xlsx"))
hypeR::hyp_to_excel(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, file_path=file.path(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_enrichment_C5_GOMF.xlsx"))
# plotting enrichment results ----
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOBP: T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOCC: T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()
TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title="GOMF:T+NK timepoint2 vs T-only timepoint1 CD")+theme_bw()
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOCC_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOCC_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOMF_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOMF_plot,
width = 20, height = 20, units = "cm")
###### For publication ######
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_AB_hyp_GOBP_plot.eps"),
plot=TpNK_tp2_vs_Tonly_tp1_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")Highlighting genes of interest
Volcano plot highlighting genes of interest:
# volcano plot
# heatmap - corrected
genes_of_interest## $Denn2b
## [1] "ENSMUSG00000031024"
##
## $Gbp6
## [1] "ENSMUSG00000104713"
##
## $`H2-K1`
## [1] "ENSMUSG00000061232"
##
## $Hs3st2
## [1] "ENSMUSG00000046321"
##
## $Htra3
## [1] "ENSMUSG00000029096"
##
## $Ifi27
## [1] "ENSMUSG00000064215"
##
## $Igtp
## [1] "ENSMUSG00000078853"
##
## $Irf1
## [1] "ENSMUSG00000018899"
##
## $Lgals3bp
## [1] "ENSMUSG00000033880"
##
## $Ly6a
## [1] "ENSMUSG00000075602"
##
## $Plaat3
## [1] "ENSMUSG00000060675"
##
## $Rtp4
## [1] "ENSMUSG00000033355"
##
## $Serpina3f
## [1] "ENSMUSG00000066363"
##
## $Stat1
## [1] "ENSMUSG00000026104"genes_of_interest_exp9.4_9.7## $Ifi44
## [1] "ENSMUSG00000028037"
##
## $Ccl5
## [1] "ENSMUSG00000035042"
##
## $Iigp1
## [1] "ENSMUSG00000054072"
##
## $Serpina3f
## [1] "ENSMUSG00000066363"
##
## $Ly6a
## [1] "ENSMUSG00000075602"
##
## $Plaat3
## [1] "ENSMUSG00000060675"
##
## $Lgals3bp
## [1] "ENSMUSG00000033880"
##
## $Ifi27
## [1] "ENSMUSG00000064215"
##
## $Gbp6
## [1] "ENSMUSG00000104713"
##
## $Rtp4
## [1] "ENSMUSG00000033355"
##
## $Stat1
## [1] "ENSMUSG00000026104"
##
## $Tap1
## [1] "ENSMUSG00000037321"
##
## $`H2-K1`
## [1] "ENSMUSG00000061232"
##
## $B2m
## [1] "ENSMUSG00000060802"
##
## $Igtp
## [1] "ENSMUSG00000078853"
##
## $Gtf2ird1
## [1] "ENSMUSG00000023079"
##
## $Cdc42ep4
## [1] "ENSMUSG00000041598"
##
## $Hoxb6
## [1] "ENSMUSG00000000690"
##
## $Bfsp2
## [1] "ENSMUSG00000032556"
##
## $Sox6
## [1] "ENSMUSG00000051910"
##
## $Tgm2
## [1] "ENSMUSG00000037820"
##
## $Adprhl1
## [1] "ENSMUSG00000031448"TpNK_tp2_vs_Tonly_tp1_volcano_plot_1 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest),
plot_title = "T+NK tp2 vs T-only tp1 CD genes set 1")
TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest_exp9.4_9.7),
plot_title = "T+NK tp2 vs T-only tp1 CD genes set 2")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot_gene_set_1.png"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_1,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot_gene_set_2.png"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
width = 20, height = 20, units = "cm")
##### for publication ######
TpNK_tp2_vs_Tonly_tp1_volcano_plot_2 <- plotVolcano_repel(dds_results_obj = deg_TpNK_tp2_vs_Tonly_tp1_results$results_all,
genes_of_interest = names(genes_of_interest_exp9.4_9.7),
plot_title = "T+NK tp2 vs T-only tp1 CD")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot.png"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
width = 18, height = 12, units = "cm")
ggsave(filename = paste0(deg_dir,"publication/", "TpNK_tp2_vs_Tonly_tp1_CD_volcano_plot.eps"),
plot=TpNK_tp2_vs_Tonly_tp1_volcano_plot_2,
width = 18, height = 12, units = "cm")print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_1)
print(TpNK_tp2_vs_Tonly_tp1_volcano_plot_2)
Heatmap of normalized and scaled expression of 321 significantly differentially expressed genes before and after removeal of batch effects coming from different experiments:
# heatmap and heatmap batch corrected - corrected
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # 
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_batch_corrected.png"),
plot=heatmap_corrected,
width = 20, height = 20, units = "cm")
###### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_bathc_corrected.png"),
plot=heatmap_corrected,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_batch_corrected.eps"),
plot=heatmap_corrected,
width = 20, height = 20, units = "cm")# identify the 25 gene set
minL2FC_Ids <- order(deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
maxL2FC_Ids <- order(decreasing = T, deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$log2FoldChange)[1:25]
L2FC_EMSEMBL <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id[c(minL2FC_Ids, maxL2FC_Ids)]
L2FC_EMSEMBL <- c(L2FC_EMSEMBL, "ENSMUSG00000075602", "ENSMUSG00000060675")
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% L2FC_EMSEMBL, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
rownames(heatmap_counts_NObatch_deg_ord) <- ensembl2symbol_annot[match(x = row.names(heatmap_counts_NObatch_deg_ord), table = ensembl2symbol_annot$ensembl_id),]$mgi_symbol
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - Batch Corrected 50 most regulated genes",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = T,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # <img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 CD”small heatmap”-1.png” width=“672” />
##### For publication #####
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected_50_genes.eps"),
plot=heatmap_corrected,
width = 24, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected_50_genes.png"),
plot=heatmap_corrected,
width = 24, height = 20, units = "cm")# heatmap and heatmap batch corrected - corrected
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#4682B4", EXP9.5 = "#7846B4", EXP9.6 = "#B47846", EXP9.7 = "#82B446", EXP9.8 = "grey")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - removed experiment BatchEffect",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # 
ggsave(filename = paste0(deg_dir, "TpNK_tp2_vs_Tonly_tp1_CD_heatmap_corrected.png"),
plot=heatmap_corrected,
width = 20, height = 20, units = "cm")AB_CD_UP_venn <- list("AB_up" = AB_results$results_signif[AB_results$results_signif$log2FoldChange > 0,]$mgi_symbol,
"CD_up" = CD_results$results_signif[CD_results$results_signif$log2FoldChange > 0,]$mgi_symbol)
AB_CD_DOWN_venn <- list("AB_down" = AB_results$results_signif[AB_results$results_signif$log2FoldChange < 0,]$mgi_symbol,
"CD_down" = CD_results$results_signif[CD_results$results_signif$log2FoldChange < 0,]$mgi_symbol)
library(ggvenn)
AB_CD_UP_venn_plot <- ggvenn(
AB_CD_UP_venn,
fill_color = c("#E0E2E3", "#8EA7CE"),
stroke_size = 0.5, set_name_size = 4
)
AB_CD_DOWN_venn_plot <- ggvenn(
AB_CD_DOWN_venn,
fill_color = c("#E0E2E3", "#8EA7CE"),
stroke_size = 0.5, set_name_size = 4
)
deg_name <- "TpNK_tp2_AB_CD_comparison"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
intersect(AB_results$results_signif[AB_results$results_signif$log2FoldChange < 0,]$mgi_symbol,
CD_results$results_signif[CD_results$results_signif$log2FoldChange < 0,]$mgi_symbol)## [1] "Bfsp2" "Hspb8" "Ckap4" "Denn2b" "Hs3st2" "Mgll" "Cdc42ep4" "Egfl7"
## [9] "Akr1c13" "Apbb1" "Pygl" "Tcf7l1" "Gria2" "Tmem176a" "Golm1" "Ntrk3"
## [17] "Tmem255a" "Tmem38b" "Gm36120" "Arid5b" "n-R5s118" "H2ac15" "Ramp2" "Tmed6"
## [25] "Rgs7bp" "Ccdc120" "Slc47a1"ggsave(filename = paste0(deg_dir, "AB_CD_UP_venn_plot.png"),
plot=AB_CD_UP_venn_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "AB_CD_UP_venn_plot.eps"),
plot=AB_CD_UP_venn_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "AB_CD_DOWN_venn_plot.png"),
plot=AB_CD_DOWN_venn_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, "AB_CD_DOWN_venn_plot.eps"),
plot=AB_CD_DOWN_venn_plot,
width = 20, height = 20, units = "cm")
#aggreagated excel sheet for
Deg_AB_xlsx <- openxlsx::read.xlsx("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_AB/deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx",sheet = 'results_signif')
Deg_CD_xlsx <- openxlsx::read.xlsx("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_CD/deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx",sheet = 'results_signif')
AB_specific <- Deg_AB_xlsx[Deg_AB_xlsx$mgi_symbol %in% setdiff(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol),]%>%
select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
CD_specific <- Deg_CD_xlsx[Deg_CD_xlsx$mgi_symbol %in% setdiff(Deg_CD_xlsx$mgi_symbol, Deg_AB_xlsx$mgi_symbol),]%>%
select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
AB_intersect_tmp <- Deg_AB_xlsx[which(Deg_AB_xlsx$mgi_symbol %in% intersect(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol)), ] %>%
select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj") %>%
rename("AB_log2FoldChange" = "log2FoldChange", "AB_padj" = "padj")
CD_intersect_tmp <- Deg_CD_xlsx[which(Deg_CD_xlsx$mgi_symbol %in% intersect(Deg_AB_xlsx$mgi_symbol, Deg_CD_xlsx$mgi_symbol)), ] %>%
select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj") %>%
rename("CD_log2FoldChange" = "log2FoldChange", "CD_padj" = "padj")
CD_intersect_tmp <- CD_intersect_tmp[match(AB_intersect_tmp$mgi_symbol, CD_intersect_tmp$mgi_symbol),]
AB_CD_intersect <- cbind(AB_intersect_tmp, select(CD_intersect_tmp, "CD_log2FoldChange", "CD_padj"))
AB_CD_ABCD_diff_intersect <- list("AB_specific" = AB_specific,
"CD_specific" = CD_specific,
"AB_CD_intersect" = AB_CD_intersect)
openxlsx::write.xlsx(AB_CD_ABCD_diff_intersect,
"~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/AB_CD_diff_and_interactions.xlsx")Make a small heatmap with genes, overlaping from AB and CD.
deg_name <- "TpNK_tp2_AB_CD_comparison"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")
AB_CD_intersect_genes <- intersect(AB_results$results_signif$mgi_symbol, CD_results$results_signif$mgi_symbol)
AB_res_subset <- AB_results$results_signif[AB_results$results_signif$mgi_symbol %in% AB_CD_intersect_genes,]
#CD_res_subset <- CD_results$results_signif[CD_results$results_signif$mgi_symbol %in% AB_CD_intersect_genes,]
AB_res_subset_UP <- AB_res_subset[AB_res_subset$log2FoldChange > 0,]
AB_res_subset_DOWN <- AB_res_subset[AB_res_subset$log2FoldChange < 0,]
#AB_res_subset_UP <- AB_res_subset_UP %>% arrange(desc(log2FoldChange)) %>% slice_head(n=20)
#AB_res_subset_DOWN <- AB_res_subset_DOWN %>% arrange(log2FoldChange) %>% slice_head(n=10)
#OR padj
AB_res_subset_UP <- AB_res_subset_UP %>% arrange(padj) %>% slice_head( n = 20)
AB_res_subset_DOWN <- AB_res_subset_DOWN %>% arrange(desc(padj)) %>% slice_head(n=10)
geneset <- c(AB_res_subset_UP$mgi_symbol, AB_res_subset_DOWN$mgi_symbol)
deg_name <- "TpNK_tp2_vs_Tonly_tp1_AB"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
load(precomputed_objects_deg_file)
load(precomputed_transf_objects_deg_file)
transf_object <- vsd_subset_filt
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]
indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]
rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c( EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(A = "#E9D8A6", B = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - Batch Corrected",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = TRUE,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) <img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB_CD”heatmap - overlap of genes from AB and CD”-1.png” width=“672” />
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")
##### The same but for CD #####
deg_name <- "TpNK_tp2_vs_Tonly_tp1_CD"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
load(precomputed_objects_deg_file)
load(precomputed_transf_objects_deg_file)
transf_object <- vsd_subset_filt
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]
indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]
rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c(C = "#E9D8A6", D = "#D9BE6D")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - Batch Corrected",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = TRUE,
cluster_cols = FALSE,
cluster_rows = FALSE,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) <img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03 AB_CD”heatmap - overlap of genes from AB and CD”-2.png” width=“672” />
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")
ggsave(filename = paste0(deg_dir, "publication/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")#First, I need to load full Dataset:
experiment_name="nk_tum_immunoedit_complete"
output_dir <- "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/"
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData")
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# log2 and vsd transformed
precomputed_transf_objects_filename <- paste0(experiment_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# check if object loaded if not load
if(!exists(precomputed_objects_file)){
load(precomputed_objects_file)
load(precomputed_transf_objects_file)
}
experiment_subset <- c("EXP9.4", "EXP9.5", "EXP9.6", "EXP9.7")
condition_tp_subset <- c("Tumor_only_timepoint_1",
"Tumor_plus_NK_timepoint_2",
"Tumor_plus_NK_timepoint_1",
"Tumor_plus_WT_NK_timepoint_2")
abs_filt_samples=3
deg_design <- as.formula("~ experiment + cell_line_label + condition_tp")
dds_subset <- dds_filt[ , dds_filt$condition_tp %in% condition_tp_subset]
dds_subset <- dds_subset[ , dds_subset$experiment %in% experiment_subset]
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)##
## Tumor_only_timepoint_1 Tumor_plus_NK_timepoint_1 Tumor_plus_NK_timepoint_2
## 42 42 41dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)##
## EXP9.4 EXP9.5 EXP9.6 EXP9.7
## 18 36 35 36dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
table(dds_subset$cell_line_label)##
## A B C D
## 27 27 36 35dds_subset_filt <- filterDatasets(dds_subset,
abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size## Original dds object samples: 125 genes: 15430
## Minimum number of samples with expression: 3
## Number of filtered genes: 433
## Filtered dds object has samples: 125 genes: 14997cat("... dds\n")## ... ddsdesign(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE)
transf_batch_NObatch_experiment <- vsd_subset_filt
transf_batch_NObatch_experiment_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_experiment), transf_batch_NObatch_experiment$experiment)
SummarizedExperiment::assay(transf_batch_NObatch_experiment) <- transf_batch_NObatch_experiment_count
deg_TpNK_tp2_vs_Tonly_tp1_results <- generateResults_upd(dds_object = dds_subset_filt,
coeff_name = "condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1",
cond_numerator = "Tumor_plus_NK_timepoint_2",
cond_denominator = "Tumor_only_timepoint_1",
cond_variable="condition_tp")
deg_TpNK_tp2_vs_Tonly_tp1_results## $results_signif
## ensembl_id mgi_symbol mgi_description entrezgene
## 1 ENSMUSG00000075602 Ly6a lymphocyte antigen 6 complex, locus A 110454
## 2 ENSMUSG00000060675 Plaat3 phospholipase A and acyltransferase 3 225845
## 3 ENSMUSG00000033880 Lgals3bp lectin, galactoside-binding, soluble, 3 binding protein 19039
## 4 ENSMUSG00000064215 Ifi27 interferon, alpha-inducible protein 27 52668
## 5 ENSMUSG00000104713 Gbp6 guanylate binding protein 6 100702
## 6 ENSMUSG00000033355 Rtp4 receptor transporter protein 4 67775
## 7 ENSMUSG00000026104 Stat1 signal transducer and activator of transcription 1 20846
## 8 ENSMUSG00000054072 Iigp1 interferon inducible GTPase 1 60440
## 9 ENSMUSG00000061232 H2-K1 histocompatibility 2, K1, K region 14972
## 10 ENSMUSG00000073555 Gm4951 predicted gene 4951 240327
## baseMean MeanExpr_Tumor_only_timepoint_1 MeanExpr_Tumor_plus_NK_timepoint_2 log2FoldChange
## 1 2456.40621 317.387691 3080.57543 3.2258807
## 2 242.32525 61.223411 324.05583 2.3592201
## 3 96.84177 11.627393 119.52526 3.3142561
## 4 364.12250 153.624891 426.71025 1.4770706
## 5 128.50281 6.047601 135.68641 4.1428494
## 6 39.88500 3.053751 46.64006 3.7056800
## 7 342.79939 79.385613 393.15858 1.9873128
## 8 1112.75679 42.307708 1175.32669 4.3614229
## 9 4893.62031 3035.265875 5424.20556 0.8243463
## 10 95.71447 10.450136 109.08363 2.9734537
## lfcSE FoldChange pvalue padj gene_biotype chromosome_name start_position
## 1 0.10740032 9.355928 9.066006e-206 1.331071e-201 protein_coding 15 74994877
## 2 0.09136706 5.130929 2.798105e-150 2.054089e-146 protein_coding 19 7557459
## 3 0.15494094 9.946963 1.049868e-103 5.138053e-100 protein_coding 11 118392751
## 4 0.06968169 2.783829 6.176427e-102 2.267058e-98 protein_coding 12 103434211
## 5 0.23046748 17.665338 3.423172e-77 1.005180e-73 protein_coding 5 105270702
## 6 0.21981886 13.047305 6.073340e-65 1.486146e-61 protein_coding 16 23520291
## 7 0.12601002 3.964978 5.014857e-61 1.051830e-57 protein_coding 1 52119440
## 8 0.29663413 20.555077 5.585136e-58 1.025012e-54 protein_coding 18 60376027
## 9 0.05356110 1.770732 6.395728e-55 1.043356e-51 protein_coding 17 33996017
## 10 0.20013176 7.854142 1.474542e-54 2.164923e-51 protein_coding 18 60212080
## end_position strand Tumor_only_timepoint_1-S_4_R0108 Tumor_only_timepoint_1-S_5_R0108
## 1 74998031 -1 226.3811564 341.817725
## 2 7588545 1 28.0879052 43.238856
## 3 118402092 -1 1.7388754 14.990335
## 4 103440239 1 98.6588702 122.576546
## 5 105293698 -1 1.7271571 14.889315
## 6 23614222 1 0.8639205 5.319718
## 7 52161865 1 74.6331721 79.944903
## 8 60392634 1 29.1684966 69.538617
## 9 34000347 -1 2565.3386318 2781.017989
## 10 60247820 1 7.8307557 15.001477
## Tumor_only_timepoint_1-S_6_R0108 Tumor_only_timepoint_1-S_7_R0108
## 1 288.630468 148.275585
## 2 39.019215 34.333732
## 3 6.625686 13.855399
## 4 100.911393 160.638370
## 5 3.290518 5.004374
## 6 0.000000 2.503178
## 7 61.756281 70.215071
## 8 59.599173 54.614600
## 9 2583.232844 2773.171511
## 10 16.576527 5.042072
## Tumor_only_timepoint_1-S_8_R0108 Tumor_only_timepoint_1-S_9_R0108
## 1 273.597302 161.953437
## 2 65.456486 53.728279
## 3 18.428030 6.908138
## 4 155.662949 139.898918
## 5 12.920360 0.000000
## 6 7.539861 6.864301
## 7 109.368560 91.134837
## 8 118.646154 30.616653
## 9 2779.194878 2806.570115
## 10 10.848075 11.522122
## Tumor_only_timepoint_1-S_34_R0108 Tumor_only_timepoint_1-S_35_R0108
## 1 297.822212 351.120560
## 2 64.761167 60.833866
## 3 6.168086 4.108498
## 4 75.784308 108.644603
## 5 6.126519 5.441081
## 6 1.225789 4.082427
## 7 71.821417 79.658971
## 8 30.861415 41.956265
## 9 3064.580619 3345.985933
## 10 9.876273 8.223104
## Tumor_only_timepoint_1-S_36_R0108 Tumor_only_timepoint_1-S_37_R0108
## 1 420.097841 290.666139
## 2 47.844025 43.135620
## 3 8.124189 2.359208
## 4 78.863488 126.747439
## 5 16.138880 0.000000
## 6 6.727196 0.000000
## 7 106.799762 101.535313
## 8 41.444818 27.141134
## 9 2950.083088 3153.145857
## 10 18.970531 2.360961
## Tumor_only_timepoint_1-S_38_R0108 Tumor_only_timepoint_1-S_39_R0108
## 1 402.564866 406.966531
## 2 57.841038 84.857099
## 3 9.171110 19.052192
## 4 178.052411 166.780680
## 5 7.085016 6.678988
## 6 2.025092 4.454422
## 7 97.821519 96.012927
## 8 82.837569 97.044997
## 9 3091.523270 3465.008042
## 10 12.237236 12.337052
## Tumor_only_timepoint_1-S_40_R0108 Tumor_only_timepoint_1-S_41_R0108
## 1 316.059288 457.71021
## 2 55.206801 64.08933
## 3 16.929106 17.31344
## 4 139.644242 135.22537
## 5 6.005364 9.55376
## 6 4.806194 6.69028
## 7 52.263737 88.42372
## 8 28.980145 101.49043
## 9 2911.020108 3081.24480
## 10 14.521448 23.10175
## Tumor_only_timepoint_1-S_42_R0108 Tumor_only_timepoint_1-S_43_R0108
## 1 480.48704 199.771467
## 2 54.28901 81.112141
## 3 32.73265 20.566199
## 4 185.68004 215.221101
## 5 19.73947 9.676233
## 6 4.64642 3.226688
## 7 103.96059 87.287023
## 8 104.66037 34.836180
## 9 3006.12358 3010.527465
## 10 14.03871 11.915597
## Tumor_only_timepoint_1-S_44_R0108 Tumor_only_timepoint_1-S_45_R0108
## 1 229.257848 203.016839
## 2 101.769822 60.487194
## 3 19.150569 18.594181
## 4 203.126981 201.245183
## 5 0.000000 0.000000
## 6 1.189315 8.211639
## 7 95.951230 66.074768
## 8 44.097532 36.432202
## 9 2601.755822 3026.664063
## 10 5.989001 8.270223
## Tumor_only_timepoint_1-S_70_R0108 Tumor_only_timepoint_1-S_71_R0108
## 1 440.139715 323.892923
## 2 48.336005 57.581463
## 3 11.796622 9.871678
## 4 110.169108 89.218043
## 5 15.232262 8.715691
## 6 1.172176 1.089893
## 7 100.600029 66.246309
## 8 158.691062 73.283414
## 9 2943.392418 3247.204251
## 10 7.083234 7.683679
## Tumor_only_timepoint_1-S_72_R0108 Tumor_only_timepoint_1-S_73_R0108
## 1 265.136089 459.069572
## 2 28.606686 43.864671
## 3 6.839694 19.749736
## 4 111.856885 106.104620
## 5 0.000000 22.559139
## 6 3.883595 4.906103
## 7 94.042735 90.517228
## 8 37.192036 104.972199
## 9 2796.872346 2890.697668
## 10 4.889127 13.835091
## Tumor_only_timepoint_1-S_74_R0108 Tumor_only_timepoint_1-S_75_R0108
## 1 354.799205 318.324971
## 2 57.523615 59.434830
## 3 5.907787 4.709775
## 4 85.429245 83.949419
## 5 3.520785 7.017053
## 6 1.174060 2.339944
## 7 84.495940 81.791847
## 8 14.314338 39.161395
## 9 2474.143255 2743.189753
## 10 15.371664 10.604869
## Tumor_only_timepoint_1-S_76_R0108 Tumor_only_timepoint_1-S_77_R0108
## 1 174.547255 289.569693
## 2 70.389334 88.539703
## 3 6.577640 13.916724
## 4 100.399813 165.883122
## 5 0.000000 15.797644
## 6 5.228720 5.926462
## 7 53.714433 82.068469
## 8 23.102968 86.155960
## 9 2747.646997 3257.905502
## 10 3.949517 11.937487
## Tumor_only_timepoint_1-S_78_R0108 Tumor_only_timepoint_1-S_79_R0108
## 1 226.481125 217.422651
## 2 52.089351 71.157408
## 3 6.879499 12.983855
## 4 114.182699 178.061376
## 5 11.388562 8.290515
## 6 1.139307 9.215331
## 7 74.665224 70.239239
## 8 43.979256 44.271868
## 9 2742.242177 3008.792871
## 10 11.474353 9.281076
## Tumor_only_timepoint_1-S_80_R0108 Tumor_only_timepoint_1-S_81_R0108
## 1 237.785720 199.559990
## 2 65.070061 73.968560
## 3 9.310747 13.759217
## 4 193.700233 226.385809
## 5 6.473601 9.461419
## 6 3.700666 7.361795
## 7 81.003262 83.840786
## 8 48.784798 33.382367
## 9 2553.959914 2921.514493
## 10 11.181201 10.591880
## Tumor_only_timepoint_1-S_1_R0114_L5311 Tumor_only_timepoint_1-S_2_R0114_L5311
## 1 505.9655486 534.8699989
## 2 60.9952416 83.8498816
## 3 6.0972113 9.4535404
## 4 138.0414336 148.5011370
## 5 1.1011131 1.1177423
## 6 0.5507746 0.6709679
## 7 88.9047202 39.0957467
## 8 1.5496474 4.3073388
## 9 4343.4360644 4697.6721201
## 10 6.1017433 1.3515096
## Tumor_only_timepoint_1-S_3_R0114_L5311 Tumor_only_timepoint_1-S_4_R0114_L5311
## 1 398.278202 349.7592985
## 2 81.552390 20.4637753
## 3 0.000000 12.7626253
## 4 125.031832 117.7537527
## 5 4.732983 2.2370502
## 6 0.000000 0.0000000
## 7 74.366562 76.4479256
## 8 3.227663 0.6996231
## 9 3541.985255 2258.0354224
## 10 3.577009 6.7617062
## Tumor_only_timepoint_1-S_5_R0114_L5311 Tumor_only_timepoint_1-S_6_R0114_L5311
## 1 400.4039886 378.5871716
## 2 36.2312671 32.1178943
## 3 3.6620692 0.7574725
## 4 150.1706045 186.3597761
## 5 0.7274781 0.0000000
## 6 2.1832985 0.0000000
## 7 83.9293788 81.2914481
## 8 5.2486655 4.9412632
## 9 3216.1610799 2904.2991488
## 10 16.8580396 5.3062488
## Tumor_only_timepoint_1-S_7_R0114_L5311 Tumor_only_timepoint_1-S_8_R0114_L5311
## 1 405.189817 345.041272
## 2 136.987058 66.942635
## 3 27.320185 11.367242
## 4 270.690680 216.884210
## 5 2.713607 1.254515
## 6 0.000000 0.000000
## 7 56.265323 64.204041
## 8 0.000000 1.177025
## 9 4556.187967 2642.948039
## 10 17.315645 6.319829
## Tumor_only_timepoint_1-S_9_R0114_L5311 Tumor_only_timepoint_1-S_10_R0114_L5311
## 1 321.561946 303.5678649
## 2 64.563574 57.1540398
## 3 4.263492 10.2217886
## 4 189.936624 226.7748238
## 5 4.940553 0.0000000
## 6 0.000000 1.5626037
## 7 65.269987 83.8668240
## 8 5.284401 0.7327513
## 9 3465.607292 3154.0607381
## 10 4.977771 5.5081311
## Tumor_only_timepoint_1-S_11_R0114_L5311 Tumor_only_timepoint_1-S_12_R0114_L5311
## 1 177.4169334 206.715578
## 2 85.4001696 88.472046
## 3 26.9457231 16.380283
## 4 240.2594438 283.137834
## 5 0.0000000 2.440484
## 6 0.8924911 4.882902
## 7 62.2162061 60.448264
## 8 3.3481249 5.148823
## 9 2508.3364761 2868.686873
## 10 25.1680349 13.113966
## Tumor_plus_NK_timepoint_2-S_16_R0108 Tumor_plus_NK_timepoint_2-S_17_R0108
## 1 1747.63424 2043.64240
## 2 181.27471 176.89550
## 3 84.16812 115.67506
## 4 379.78296 376.28100
## 5 28.98165 32.40643
## 6 42.37457 63.85612
## 7 122.06926 141.00851
## 8 106.58488 116.17965
## 9 6905.47407 6449.94761
## 10 24.70767 37.59760
## Tumor_plus_NK_timepoint_2-S_18_R0108 Tumor_plus_NK_timepoint_2-S_19_R0108
## 1 1961.66038 2169.31465
## 2 208.42627 252.79548
## 3 131.79911 126.70198
## 4 393.85502 640.51907
## 5 38.00639 67.76438
## 6 63.36908 58.10676
## 7 166.78004 210.28623
## 8 203.35784 306.30693
## 9 6295.43975 5827.26955
## 10 44.67482 32.51184
## Tumor_plus_NK_timepoint_2-S_20_R0108 Tumor_plus_NK_timepoint_2-S_21_R0108
## 1 1663.59370 1719.85123
## 2 297.94389 307.26016
## 3 148.16929 124.86695
## 4 566.98286 551.29567
## 5 40.31177 56.01150
## 6 59.30549 44.02647
## 7 197.19845 203.08716
## 8 240.53894 271.33505
## 9 5807.39212 5961.36343
## 10 27.47515 40.30960
## Tumor_plus_NK_timepoint_2-S_58_R0108 Tumor_plus_NK_timepoint_2-S_59_R0108
## 1 6488.80633 6112.69599
## 2 503.96062 519.00145
## 3 197.54182 172.40962
## 4 600.59927 521.49861
## 5 549.18734 461.83058
## 6 82.96721 61.25829
## 7 1193.43729 1284.76246
## 8 4667.39493 4185.81629
## 9 6607.43373 6610.74395
## 10 335.25549 308.47662
## Tumor_plus_NK_timepoint_2-S_60_R0108 Tumor_plus_NK_timepoint_2-S_61_R0108
## 1 6934.22563 2570.28564
## 2 517.09769 326.79839
## 3 141.77686 120.59119
## 4 608.79137 319.08203
## 5 528.39189 77.95110
## 6 57.34824 23.77499
## 7 1221.99259 292.64331
## 8 4105.83752 573.06006
## 9 6155.02388 5435.19475
## 10 297.57603 69.91826
## Tumor_plus_NK_timepoint_2-S_62_R0108 Tumor_plus_NK_timepoint_2-S_63_R0108
## 1 3412.69962 2379.91330
## 2 331.81238 309.76795
## 3 128.80487 114.34307
## 4 353.07839 340.29375
## 5 176.34539 130.09215
## 6 17.29561 17.55907
## 7 384.82494 274.10965
## 8 1045.62747 621.84628
## 9 5600.59252 5799.93438
## 10 130.06187 100.90474
## Tumor_plus_NK_timepoint_2-S_64_R0108 Tumor_plus_NK_timepoint_2-S_65_R0108
## 1 4339.3412 4841.7061
## 2 301.9529 342.1000
## 3 213.3512 193.8010
## 4 443.5946 409.1050
## 5 146.5486 243.3223
## 6 147.5576 123.3321
## 7 606.5484 629.9075
## 8 2337.2736 2906.8185
## 9 6228.9387 5952.1540
## 10 212.5692 223.3637
## Tumor_plus_NK_timepoint_2-S_66_R0108 Tumor_plus_NK_timepoint_2-S_67_R0108
## 1 4053.6859 5411.1298
## 2 318.4661 499.6300
## 3 194.3493 286.8203
## 4 331.4864 617.1513
## 5 179.4221 349.3431
## 6 104.8067 156.6732
## 7 612.9029 971.6622
## 8 2402.8629 4499.3759
## 9 5620.9965 6559.9749
## 10 166.1504 379.5810
## Tumor_plus_NK_timepoint_2-S_68_R0108 Tumor_plus_NK_timepoint_2-S_69_R0108
## 1 5068.2301 5785.6571
## 2 571.7537 618.9396
## 3 212.2716 185.3203
## 4 647.7775 565.9427
## 5 290.4707 298.7725
## 6 121.8675 123.6037
## 7 715.3963 824.1747
## 8 3518.5242 3694.0642
## 9 5686.0770 5153.7038
## 10 276.1582 307.4031
## Tumor_plus_NK_timepoint_2-S_94_R0108 Tumor_plus_NK_timepoint_2-S_95_R0108
## 1 2853.68834 3907.22481
## 2 315.31412 408.27858
## 3 98.55720 102.00342
## 4 376.87513 444.90837
## 5 136.49872 189.47599
## 6 12.41389 48.88943
## 7 369.86090 525.84934
## 8 582.78057 1490.16889
## 9 6266.28309 5556.06367
## 10 75.01471 102.07924
## Tumor_plus_NK_timepoint_2-S_96_R0108 Tumor_plus_NK_timepoint_2-S_97_R0108
## 1 3054.755772 1612.10441
## 2 343.314502 268.09793
## 3 48.835770 79.95366
## 4 407.082559 210.56451
## 5 127.347277 48.09632
## 6 7.643862 0.00000
## 7 368.781959 129.49845
## 8 556.921078 155.88130
## 9 5219.548551 4777.95969
## 10 80.833154 24.79279
## Tumor_plus_NK_timepoint_2-S_98_R0108 Tumor_plus_NK_timepoint_2-S_99_R0108
## 1 3161.130849 3365.620217
## 2 335.293619 353.560196
## 3 94.332083 91.340829
## 4 258.349255 233.479909
## 5 151.191882 143.746550
## 6 9.586379 4.714868
## 7 397.607171 488.790691
## 8 964.039135 1354.323735
## 9 4752.982702 5356.586333
## 10 101.911465 115.151248
## Tumor_plus_NK_timepoint_2-S_100_R0108 Tumor_plus_NK_timepoint_2-S_101_R0108
## 1 2248.20859 3693.7522
## 2 285.03959 343.1971
## 3 126.88052 119.1217
## 4 413.17662 357.6320
## 5 63.01274 143.9118
## 6 48.40394 50.0309
## 7 229.62176 406.1431
## 8 317.53985 1435.0022
## 9 5123.10203 5736.0080
## 10 41.94704 99.5467
## Tumor_plus_NK_timepoint_2-S_102_R0108 Tumor_plus_NK_timepoint_2-S_103_R0108
## 1 2497.46950 3970.89731
## 2 257.96566 393.79609
## 3 99.21820 230.91655
## 4 419.01959 561.65120
## 5 58.51867 146.73088
## 6 31.59401 83.58817
## 7 224.34658 568.56385
## 8 377.55128 1663.47315
## 9 4886.22860 5833.79498
## 10 59.89536 144.75629
## Tumor_plus_NK_timepoint_2-S_104_R0108 Tumor_plus_NK_timepoint_2-S_25_R0114_L5311
## 1 4100.50705 2451.84810
## 2 442.56640 275.66064
## 3 141.80837 24.51542
## 4 678.64953 438.98600
## 5 212.31066 53.73839
## 6 67.29958 5.03997
## 7 520.66380 152.25959
## 8 1819.02296 112.74574
## 9 6454.32469 6012.40889
## 10 140.85471 48.22130
## Tumor_plus_NK_timepoint_2-S_26_R0114_L5311 Tumor_plus_NK_timepoint_2-S_27_R0114_L5311
## 1 1413.182567 2255.761783
## 2 196.748054 230.346953
## 3 13.427171 22.182839
## 4 312.802010 398.735564
## 5 42.510684 23.607159
## 6 7.504856 8.796351
## 7 169.835020 135.128724
## 8 130.822084 87.631803
## 9 3255.278358 3864.183482
## 10 42.830920 12.685330
## Tumor_plus_NK_timepoint_2-S_28_R0114_L5311 Tumor_plus_NK_timepoint_2-S_29_R0114_L5311
## 1 1512.3967890 3662.44856
## 2 139.5622890 286.44692
## 3 57.1499529 142.65577
## 4 240.5679225 427.38939
## 5 31.4507777 128.38609
## 6 0.7673959 10.18098
## 7 119.9576377 369.55434
## 8 25.1898319 906.82660
## 9 3509.5858480 4351.23008
## 10 22.4132504 124.62080
## Tumor_plus_NK_timepoint_2-S_30_R0114_L5311 Tumor_plus_NK_timepoint_2-S_31_R0114_L5311
## 1 2026.09662 1379.749209
## 2 151.23444 166.924585
## 3 65.04070 64.058822
## 4 311.23758 310.013115
## 5 44.45756 29.130493
## 6 0.00000 9.202746
## 7 216.07682 122.625541
## 8 138.24193 50.223875
## 9 3933.19026 4127.948022
## 10 42.69281 32.439402
## Tumor_plus_NK_timepoint_2-S_32_R0114_L5311 Tumor_plus_NK_timepoint_2-S_33_R0114_L5311
## 1 1007.45324 1090.70706
## 2 121.56246 137.02984
## 3 22.86767 34.30605
## 4 299.26832 256.26347
## 5 12.45582 17.03743
## 6 11.72777 10.37472
## 7 85.44529 88.40889
## 8 22.68844 18.53216
## 9 3051.54335 3363.10199
## 10 34.69610 26.12183
## Tumor_plus_NK_timepoint_2-S_34_R0114_L5311 Tumor_plus_NK_timepoint_2-S_35_R0114_L5311
## 1 2545.25905 1889.68993
## 2 475.60652 375.51234
## 3 138.20748 91.00698
## 4 599.21008 413.73002
## 5 20.33720 16.11670
## 6 79.92778 25.93714
## 7 170.30992 117.15614
## 8 77.83998 52.85539
## 9 6938.32427 5117.73916
## 10 30.00380 25.41617
## Tumor_plus_NK_timepoint_2-S_36_R0114_L5311
## 1 1899.567509
## 2 397.353609
## 3 99.386914
## 4 458.410596
## 5 27.913123
## 6 9.535085
## 7 90.224031
## 8 45.286888
## 9 6247.357109
## 10 28.809331
## [ reached 'max' / getOption("max.print") -- omitted 480 rows ]
##
## $de_details
## test
## 1 condition_tp_Tumor_plus_NK_timepoint_2_vs_Tumor_only_timepoint_1
## design signif_genes signif_genes_UP signif_genes_DOWN
## 1 ~experiment + cell_line_label + condition_tp 490 330 160
## cutoffs
## 1 (!is.na(padj) & (padj < 0.05)) & abs(log2FoldChange) > 0.58
##
## $results_all
## ensembl_id mgi_symbol mgi_description entrezgene
## 1 ENSMUSG00000075602 Ly6a lymphocyte antigen 6 complex, locus A 110454
## 2 ENSMUSG00000060675 Plaat3 phospholipase A and acyltransferase 3 225845
## 3 ENSMUSG00000033880 Lgals3bp lectin, galactoside-binding, soluble, 3 binding protein 19039
## 4 ENSMUSG00000064215 Ifi27 interferon, alpha-inducible protein 27 52668
## 5 ENSMUSG00000104713 Gbp6 guanylate binding protein 6 100702
## 6 ENSMUSG00000033355 Rtp4 receptor transporter protein 4 67775
## 7 ENSMUSG00000026104 Stat1 signal transducer and activator of transcription 1 20846
## 8 ENSMUSG00000054072 Iigp1 interferon inducible GTPase 1 60440
## 9 ENSMUSG00000061232 H2-K1 histocompatibility 2, K1, K region 14972
## 10 ENSMUSG00000073555 Gm4951 predicted gene 4951 240327
## baseMean MeanExpr_Tumor_only_timepoint_1 MeanExpr_Tumor_plus_NK_timepoint_2 log2FoldChange
## 1 2456.40621 317.387691 3080.57543 3.2258807
## 2 242.32525 61.223411 324.05583 2.3592201
## 3 96.84177 11.627393 119.52526 3.3142561
## 4 364.12250 153.624891 426.71025 1.4770706
## 5 128.50281 6.047601 135.68641 4.1428494
## 6 39.88500 3.053751 46.64006 3.7056800
## 7 342.79939 79.385613 393.15858 1.9873128
## 8 1112.75679 42.307708 1175.32669 4.3614229
## 9 4893.62031 3035.265875 5424.20556 0.8243463
## 10 95.71447 10.450136 109.08363 2.9734537
## lfcSE FoldChange pvalue padj gene_biotype chromosome_name start_position
## 1 0.10740032 9.355928 9.066006e-206 1.331071e-201 protein_coding 15 74994877
## 2 0.09136706 5.130929 2.798105e-150 2.054089e-146 protein_coding 19 7557459
## 3 0.15494094 9.946963 1.049868e-103 5.138053e-100 protein_coding 11 118392751
## 4 0.06968169 2.783829 6.176427e-102 2.267058e-98 protein_coding 12 103434211
## 5 0.23046748 17.665338 3.423172e-77 1.005180e-73 protein_coding 5 105270702
## 6 0.21981886 13.047305 6.073340e-65 1.486146e-61 protein_coding 16 23520291
## 7 0.12601002 3.964978 5.014857e-61 1.051830e-57 protein_coding 1 52119440
## 8 0.29663413 20.555077 5.585136e-58 1.025012e-54 protein_coding 18 60376027
## 9 0.05356110 1.770732 6.395728e-55 1.043356e-51 protein_coding 17 33996017
## 10 0.20013176 7.854142 1.474542e-54 2.164923e-51 protein_coding 18 60212080
## end_position strand Tumor_only_timepoint_1-S_4_R0108 Tumor_only_timepoint_1-S_5_R0108
## 1 74998031 -1 226.3811564 341.817725
## 2 7588545 1 28.0879052 43.238856
## 3 118402092 -1 1.7388754 14.990335
## 4 103440239 1 98.6588702 122.576546
## 5 105293698 -1 1.7271571 14.889315
## 6 23614222 1 0.8639205 5.319718
## 7 52161865 1 74.6331721 79.944903
## 8 60392634 1 29.1684966 69.538617
## 9 34000347 -1 2565.3386318 2781.017989
## 10 60247820 1 7.8307557 15.001477
## Tumor_only_timepoint_1-S_6_R0108 Tumor_only_timepoint_1-S_7_R0108
## 1 288.630468 148.275585
## 2 39.019215 34.333732
## 3 6.625686 13.855399
## 4 100.911393 160.638370
## 5 3.290518 5.004374
## 6 0.000000 2.503178
## 7 61.756281 70.215071
## 8 59.599173 54.614600
## 9 2583.232844 2773.171511
## 10 16.576527 5.042072
## Tumor_only_timepoint_1-S_8_R0108 Tumor_only_timepoint_1-S_9_R0108
## 1 273.597302 161.953437
## 2 65.456486 53.728279
## 3 18.428030 6.908138
## 4 155.662949 139.898918
## 5 12.920360 0.000000
## 6 7.539861 6.864301
## 7 109.368560 91.134837
## 8 118.646154 30.616653
## 9 2779.194878 2806.570115
## 10 10.848075 11.522122
## Tumor_only_timepoint_1-S_34_R0108 Tumor_only_timepoint_1-S_35_R0108
## 1 297.822212 351.120560
## 2 64.761167 60.833866
## 3 6.168086 4.108498
## 4 75.784308 108.644603
## 5 6.126519 5.441081
## 6 1.225789 4.082427
## 7 71.821417 79.658971
## 8 30.861415 41.956265
## 9 3064.580619 3345.985933
## 10 9.876273 8.223104
## Tumor_only_timepoint_1-S_36_R0108 Tumor_only_timepoint_1-S_37_R0108
## 1 420.097841 290.666139
## 2 47.844025 43.135620
## 3 8.124189 2.359208
## 4 78.863488 126.747439
## 5 16.138880 0.000000
## 6 6.727196 0.000000
## 7 106.799762 101.535313
## 8 41.444818 27.141134
## 9 2950.083088 3153.145857
## 10 18.970531 2.360961
## Tumor_only_timepoint_1-S_38_R0108 Tumor_only_timepoint_1-S_39_R0108
## 1 402.564866 406.966531
## 2 57.841038 84.857099
## 3 9.171110 19.052192
## 4 178.052411 166.780680
## 5 7.085016 6.678988
## 6 2.025092 4.454422
## 7 97.821519 96.012927
## 8 82.837569 97.044997
## 9 3091.523270 3465.008042
## 10 12.237236 12.337052
## Tumor_only_timepoint_1-S_40_R0108 Tumor_only_timepoint_1-S_41_R0108
## 1 316.059288 457.71021
## 2 55.206801 64.08933
## 3 16.929106 17.31344
## 4 139.644242 135.22537
## 5 6.005364 9.55376
## 6 4.806194 6.69028
## 7 52.263737 88.42372
## 8 28.980145 101.49043
## 9 2911.020108 3081.24480
## 10 14.521448 23.10175
## Tumor_only_timepoint_1-S_42_R0108 Tumor_only_timepoint_1-S_43_R0108
## 1 480.48704 199.771467
## 2 54.28901 81.112141
## 3 32.73265 20.566199
## 4 185.68004 215.221101
## 5 19.73947 9.676233
## 6 4.64642 3.226688
## 7 103.96059 87.287023
## 8 104.66037 34.836180
## 9 3006.12358 3010.527465
## 10 14.03871 11.915597
## Tumor_only_timepoint_1-S_44_R0108 Tumor_only_timepoint_1-S_45_R0108
## 1 229.257848 203.016839
## 2 101.769822 60.487194
## 3 19.150569 18.594181
## 4 203.126981 201.245183
## 5 0.000000 0.000000
## 6 1.189315 8.211639
## 7 95.951230 66.074768
## 8 44.097532 36.432202
## 9 2601.755822 3026.664063
## 10 5.989001 8.270223
## Tumor_only_timepoint_1-S_70_R0108 Tumor_only_timepoint_1-S_71_R0108
## 1 440.139715 323.892923
## 2 48.336005 57.581463
## 3 11.796622 9.871678
## 4 110.169108 89.218043
## 5 15.232262 8.715691
## 6 1.172176 1.089893
## 7 100.600029 66.246309
## 8 158.691062 73.283414
## 9 2943.392418 3247.204251
## 10 7.083234 7.683679
## Tumor_only_timepoint_1-S_72_R0108 Tumor_only_timepoint_1-S_73_R0108
## 1 265.136089 459.069572
## 2 28.606686 43.864671
## 3 6.839694 19.749736
## 4 111.856885 106.104620
## 5 0.000000 22.559139
## 6 3.883595 4.906103
## 7 94.042735 90.517228
## 8 37.192036 104.972199
## 9 2796.872346 2890.697668
## 10 4.889127 13.835091
## Tumor_only_timepoint_1-S_74_R0108 Tumor_only_timepoint_1-S_75_R0108
## 1 354.799205 318.324971
## 2 57.523615 59.434830
## 3 5.907787 4.709775
## 4 85.429245 83.949419
## 5 3.520785 7.017053
## 6 1.174060 2.339944
## 7 84.495940 81.791847
## 8 14.314338 39.161395
## 9 2474.143255 2743.189753
## 10 15.371664 10.604869
## Tumor_only_timepoint_1-S_76_R0108 Tumor_only_timepoint_1-S_77_R0108
## 1 174.547255 289.569693
## 2 70.389334 88.539703
## 3 6.577640 13.916724
## 4 100.399813 165.883122
## 5 0.000000 15.797644
## 6 5.228720 5.926462
## 7 53.714433 82.068469
## 8 23.102968 86.155960
## 9 2747.646997 3257.905502
## 10 3.949517 11.937487
## Tumor_only_timepoint_1-S_78_R0108 Tumor_only_timepoint_1-S_79_R0108
## 1 226.481125 217.422651
## 2 52.089351 71.157408
## 3 6.879499 12.983855
## 4 114.182699 178.061376
## 5 11.388562 8.290515
## 6 1.139307 9.215331
## 7 74.665224 70.239239
## 8 43.979256 44.271868
## 9 2742.242177 3008.792871
## 10 11.474353 9.281076
## Tumor_only_timepoint_1-S_80_R0108 Tumor_only_timepoint_1-S_81_R0108
## 1 237.785720 199.559990
## 2 65.070061 73.968560
## 3 9.310747 13.759217
## 4 193.700233 226.385809
## 5 6.473601 9.461419
## 6 3.700666 7.361795
## 7 81.003262 83.840786
## 8 48.784798 33.382367
## 9 2553.959914 2921.514493
## 10 11.181201 10.591880
## Tumor_only_timepoint_1-S_1_R0114_L5311 Tumor_only_timepoint_1-S_2_R0114_L5311
## 1 505.9655486 534.8699989
## 2 60.9952416 83.8498816
## 3 6.0972113 9.4535404
## 4 138.0414336 148.5011370
## 5 1.1011131 1.1177423
## 6 0.5507746 0.6709679
## 7 88.9047202 39.0957467
## 8 1.5496474 4.3073388
## 9 4343.4360644 4697.6721201
## 10 6.1017433 1.3515096
## Tumor_only_timepoint_1-S_3_R0114_L5311 Tumor_only_timepoint_1-S_4_R0114_L5311
## 1 398.278202 349.7592985
## 2 81.552390 20.4637753
## 3 0.000000 12.7626253
## 4 125.031832 117.7537527
## 5 4.732983 2.2370502
## 6 0.000000 0.0000000
## 7 74.366562 76.4479256
## 8 3.227663 0.6996231
## 9 3541.985255 2258.0354224
## 10 3.577009 6.7617062
## Tumor_only_timepoint_1-S_5_R0114_L5311 Tumor_only_timepoint_1-S_6_R0114_L5311
## 1 400.4039886 378.5871716
## 2 36.2312671 32.1178943
## 3 3.6620692 0.7574725
## 4 150.1706045 186.3597761
## 5 0.7274781 0.0000000
## 6 2.1832985 0.0000000
## 7 83.9293788 81.2914481
## 8 5.2486655 4.9412632
## 9 3216.1610799 2904.2991488
## 10 16.8580396 5.3062488
## Tumor_only_timepoint_1-S_7_R0114_L5311 Tumor_only_timepoint_1-S_8_R0114_L5311
## 1 405.189817 345.041272
## 2 136.987058 66.942635
## 3 27.320185 11.367242
## 4 270.690680 216.884210
## 5 2.713607 1.254515
## 6 0.000000 0.000000
## 7 56.265323 64.204041
## 8 0.000000 1.177025
## 9 4556.187967 2642.948039
## 10 17.315645 6.319829
## Tumor_only_timepoint_1-S_9_R0114_L5311 Tumor_only_timepoint_1-S_10_R0114_L5311
## 1 321.561946 303.5678649
## 2 64.563574 57.1540398
## 3 4.263492 10.2217886
## 4 189.936624 226.7748238
## 5 4.940553 0.0000000
## 6 0.000000 1.5626037
## 7 65.269987 83.8668240
## 8 5.284401 0.7327513
## 9 3465.607292 3154.0607381
## 10 4.977771 5.5081311
## Tumor_only_timepoint_1-S_11_R0114_L5311 Tumor_only_timepoint_1-S_12_R0114_L5311
## 1 177.4169334 206.715578
## 2 85.4001696 88.472046
## 3 26.9457231 16.380283
## 4 240.2594438 283.137834
## 5 0.0000000 2.440484
## 6 0.8924911 4.882902
## 7 62.2162061 60.448264
## 8 3.3481249 5.148823
## 9 2508.3364761 2868.686873
## 10 25.1680349 13.113966
## Tumor_plus_NK_timepoint_2-S_16_R0108 Tumor_plus_NK_timepoint_2-S_17_R0108
## 1 1747.63424 2043.64240
## 2 181.27471 176.89550
## 3 84.16812 115.67506
## 4 379.78296 376.28100
## 5 28.98165 32.40643
## 6 42.37457 63.85612
## 7 122.06926 141.00851
## 8 106.58488 116.17965
## 9 6905.47407 6449.94761
## 10 24.70767 37.59760
## Tumor_plus_NK_timepoint_2-S_18_R0108 Tumor_plus_NK_timepoint_2-S_19_R0108
## 1 1961.66038 2169.31465
## 2 208.42627 252.79548
## 3 131.79911 126.70198
## 4 393.85502 640.51907
## 5 38.00639 67.76438
## 6 63.36908 58.10676
## 7 166.78004 210.28623
## 8 203.35784 306.30693
## 9 6295.43975 5827.26955
## 10 44.67482 32.51184
## Tumor_plus_NK_timepoint_2-S_20_R0108 Tumor_plus_NK_timepoint_2-S_21_R0108
## 1 1663.59370 1719.85123
## 2 297.94389 307.26016
## 3 148.16929 124.86695
## 4 566.98286 551.29567
## 5 40.31177 56.01150
## 6 59.30549 44.02647
## 7 197.19845 203.08716
## 8 240.53894 271.33505
## 9 5807.39212 5961.36343
## 10 27.47515 40.30960
## Tumor_plus_NK_timepoint_2-S_58_R0108 Tumor_plus_NK_timepoint_2-S_59_R0108
## 1 6488.80633 6112.69599
## 2 503.96062 519.00145
## 3 197.54182 172.40962
## 4 600.59927 521.49861
## 5 549.18734 461.83058
## 6 82.96721 61.25829
## 7 1193.43729 1284.76246
## 8 4667.39493 4185.81629
## 9 6607.43373 6610.74395
## 10 335.25549 308.47662
## Tumor_plus_NK_timepoint_2-S_60_R0108 Tumor_plus_NK_timepoint_2-S_61_R0108
## 1 6934.22563 2570.28564
## 2 517.09769 326.79839
## 3 141.77686 120.59119
## 4 608.79137 319.08203
## 5 528.39189 77.95110
## 6 57.34824 23.77499
## 7 1221.99259 292.64331
## 8 4105.83752 573.06006
## 9 6155.02388 5435.19475
## 10 297.57603 69.91826
## Tumor_plus_NK_timepoint_2-S_62_R0108 Tumor_plus_NK_timepoint_2-S_63_R0108
## 1 3412.69962 2379.91330
## 2 331.81238 309.76795
## 3 128.80487 114.34307
## 4 353.07839 340.29375
## 5 176.34539 130.09215
## 6 17.29561 17.55907
## 7 384.82494 274.10965
## 8 1045.62747 621.84628
## 9 5600.59252 5799.93438
## 10 130.06187 100.90474
## Tumor_plus_NK_timepoint_2-S_64_R0108 Tumor_plus_NK_timepoint_2-S_65_R0108
## 1 4339.3412 4841.7061
## 2 301.9529 342.1000
## 3 213.3512 193.8010
## 4 443.5946 409.1050
## 5 146.5486 243.3223
## 6 147.5576 123.3321
## 7 606.5484 629.9075
## 8 2337.2736 2906.8185
## 9 6228.9387 5952.1540
## 10 212.5692 223.3637
## Tumor_plus_NK_timepoint_2-S_66_R0108 Tumor_plus_NK_timepoint_2-S_67_R0108
## 1 4053.6859 5411.1298
## 2 318.4661 499.6300
## 3 194.3493 286.8203
## 4 331.4864 617.1513
## 5 179.4221 349.3431
## 6 104.8067 156.6732
## 7 612.9029 971.6622
## 8 2402.8629 4499.3759
## 9 5620.9965 6559.9749
## 10 166.1504 379.5810
## Tumor_plus_NK_timepoint_2-S_68_R0108 Tumor_plus_NK_timepoint_2-S_69_R0108
## 1 5068.2301 5785.6571
## 2 571.7537 618.9396
## 3 212.2716 185.3203
## 4 647.7775 565.9427
## 5 290.4707 298.7725
## 6 121.8675 123.6037
## 7 715.3963 824.1747
## 8 3518.5242 3694.0642
## 9 5686.0770 5153.7038
## 10 276.1582 307.4031
## Tumor_plus_NK_timepoint_2-S_94_R0108 Tumor_plus_NK_timepoint_2-S_95_R0108
## 1 2853.68834 3907.22481
## 2 315.31412 408.27858
## 3 98.55720 102.00342
## 4 376.87513 444.90837
## 5 136.49872 189.47599
## 6 12.41389 48.88943
## 7 369.86090 525.84934
## 8 582.78057 1490.16889
## 9 6266.28309 5556.06367
## 10 75.01471 102.07924
## Tumor_plus_NK_timepoint_2-S_96_R0108 Tumor_plus_NK_timepoint_2-S_97_R0108
## 1 3054.755772 1612.10441
## 2 343.314502 268.09793
## 3 48.835770 79.95366
## 4 407.082559 210.56451
## 5 127.347277 48.09632
## 6 7.643862 0.00000
## 7 368.781959 129.49845
## 8 556.921078 155.88130
## 9 5219.548551 4777.95969
## 10 80.833154 24.79279
## Tumor_plus_NK_timepoint_2-S_98_R0108 Tumor_plus_NK_timepoint_2-S_99_R0108
## 1 3161.130849 3365.620217
## 2 335.293619 353.560196
## 3 94.332083 91.340829
## 4 258.349255 233.479909
## 5 151.191882 143.746550
## 6 9.586379 4.714868
## 7 397.607171 488.790691
## 8 964.039135 1354.323735
## 9 4752.982702 5356.586333
## 10 101.911465 115.151248
## Tumor_plus_NK_timepoint_2-S_100_R0108 Tumor_plus_NK_timepoint_2-S_101_R0108
## 1 2248.20859 3693.7522
## 2 285.03959 343.1971
## 3 126.88052 119.1217
## 4 413.17662 357.6320
## 5 63.01274 143.9118
## 6 48.40394 50.0309
## 7 229.62176 406.1431
## 8 317.53985 1435.0022
## 9 5123.10203 5736.0080
## 10 41.94704 99.5467
## Tumor_plus_NK_timepoint_2-S_102_R0108 Tumor_plus_NK_timepoint_2-S_103_R0108
## 1 2497.46950 3970.89731
## 2 257.96566 393.79609
## 3 99.21820 230.91655
## 4 419.01959 561.65120
## 5 58.51867 146.73088
## 6 31.59401 83.58817
## 7 224.34658 568.56385
## 8 377.55128 1663.47315
## 9 4886.22860 5833.79498
## 10 59.89536 144.75629
## Tumor_plus_NK_timepoint_2-S_104_R0108 Tumor_plus_NK_timepoint_2-S_25_R0114_L5311
## 1 4100.50705 2451.84810
## 2 442.56640 275.66064
## 3 141.80837 24.51542
## 4 678.64953 438.98600
## 5 212.31066 53.73839
## 6 67.29958 5.03997
## 7 520.66380 152.25959
## 8 1819.02296 112.74574
## 9 6454.32469 6012.40889
## 10 140.85471 48.22130
## Tumor_plus_NK_timepoint_2-S_26_R0114_L5311 Tumor_plus_NK_timepoint_2-S_27_R0114_L5311
## 1 1413.182567 2255.761783
## 2 196.748054 230.346953
## 3 13.427171 22.182839
## 4 312.802010 398.735564
## 5 42.510684 23.607159
## 6 7.504856 8.796351
## 7 169.835020 135.128724
## 8 130.822084 87.631803
## 9 3255.278358 3864.183482
## 10 42.830920 12.685330
## Tumor_plus_NK_timepoint_2-S_28_R0114_L5311 Tumor_plus_NK_timepoint_2-S_29_R0114_L5311
## 1 1512.3967890 3662.44856
## 2 139.5622890 286.44692
## 3 57.1499529 142.65577
## 4 240.5679225 427.38939
## 5 31.4507777 128.38609
## 6 0.7673959 10.18098
## 7 119.9576377 369.55434
## 8 25.1898319 906.82660
## 9 3509.5858480 4351.23008
## 10 22.4132504 124.62080
## Tumor_plus_NK_timepoint_2-S_30_R0114_L5311 Tumor_plus_NK_timepoint_2-S_31_R0114_L5311
## 1 2026.09662 1379.749209
## 2 151.23444 166.924585
## 3 65.04070 64.058822
## 4 311.23758 310.013115
## 5 44.45756 29.130493
## 6 0.00000 9.202746
## 7 216.07682 122.625541
## 8 138.24193 50.223875
## 9 3933.19026 4127.948022
## 10 42.69281 32.439402
## Tumor_plus_NK_timepoint_2-S_32_R0114_L5311 Tumor_plus_NK_timepoint_2-S_33_R0114_L5311
## 1 1007.45324 1090.70706
## 2 121.56246 137.02984
## 3 22.86767 34.30605
## 4 299.26832 256.26347
## 5 12.45582 17.03743
## 6 11.72777 10.37472
## 7 85.44529 88.40889
## 8 22.68844 18.53216
## 9 3051.54335 3363.10199
## 10 34.69610 26.12183
## Tumor_plus_NK_timepoint_2-S_34_R0114_L5311 Tumor_plus_NK_timepoint_2-S_35_R0114_L5311
## 1 2545.25905 1889.68993
## 2 475.60652 375.51234
## 3 138.20748 91.00698
## 4 599.21008 413.73002
## 5 20.33720 16.11670
## 6 79.92778 25.93714
## 7 170.30992 117.15614
## 8 77.83998 52.85539
## 9 6938.32427 5117.73916
## 10 30.00380 25.41617
## Tumor_plus_NK_timepoint_2-S_36_R0114_L5311
## 1 1899.567509
## 2 397.353609
## 3 99.386914
## 4 458.410596
## 5 27.913123
## 6 9.535085
## 7 90.224031
## 8 45.286888
## 9 6247.357109
## 10 28.809331
## [ reached 'max' / getOption("max.print") -- omitted 14987 rows ]metadata_heatmap <- as.data.frame(colData(dds_subset))
transf_object <- vsd_subset_filt
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_experiment) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_TpNK_tp2_vs_Tonly_tp1_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(experiment, cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_TpNK_tp2_vs_Tonly_tp1_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
# select top 20 and 10 gens from ab cd up and down regulated
AB_res_path <- "~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_AB/deg_TpNK_tp2_vs_Tonly_tp1_AB_results.xlsx"
CD_res_path <- "~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_vs_Tonly_tp1_CD/deg_TpNK_tp2_vs_Tonly_tp1_CD_results.xlsx"
AB_results <- openxlsx::read.xlsx(xlsxFile = AB_res_path, sheet = "results_signif")
CD_results <- openxlsx::read.xlsx(xlsxFile = CD_res_path, sheet = "results_signif")
AB_CD_intersect_genes <- intersect(AB_results$mgi_symbol, CD_results$mgi_symbol)
AB_res_subset <- AB_results[AB_results$mgi_symbol %in% AB_CD_intersect_genes,]
CD_res_subset <- CD_results[CD_results$mgi_symbol %in% AB_CD_intersect_genes,]
combined_AB_CD <- rbind.data.frame(AB_res_subset[,1:14], CD_res_subset[,1:14])
AB_CD_res_subset_UP <- combined_AB_CD[combined_AB_CD$log2FoldChange > 0,]
AB_CD_res_subset_DOWN <- combined_AB_CD[combined_AB_CD$log2FoldChange < 0,]
AB_CD_res_subset_UP <- AB_CD_res_subset_UP %>% arrange(padj)
AB_CD_res_subset_UP <- AB_CD_res_subset_UP[!duplicated(AB_CD_res_subset_UP$mgi_symbol),] %>% slice_head(n=20)
AB_CD_res_subset_DOWN <- AB_CD_res_subset_DOWN %>% arrange(padj)
AB_CD_res_subset_DOWN <- AB_CD_res_subset_DOWN[!duplicated(AB_CD_res_subset_DOWN$mgi_symbol),]%>% slice_head(n=10)
geneset <- c(AB_CD_res_subset_UP$mgi_symbol, AB_CD_res_subset_DOWN$mgi_symbol)
ensembl_gene_set <- ensembl2symbol_annot[which(ensembl2symbol_annot$mgi_symbol %in% geneset),]
ensembl_gene_set <- ensembl_gene_set[ match(geneset , ensembl_gene_set$mgi_symbol),]
indexes <- which(row.names(heatmap_counts_NObatch_deg_ord) %in% ensembl_gene_set$ensembl_id)
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[indexes,]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg_ord[ match(ensembl_gene_set$ensembl_id , row.names(heatmap_counts_NObatch_deg_ord)),]
rownames(heatmap_counts_NObatch_deg_ord) <- ensembl_gene_set$mgi_symbol
######
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#00262E", EXP9.5 = "#005f73", EXP9.6 = "#0a9396", EXP9.7 = "#94d2bd"),
condition_tp = c(Tumor_only_timepoint_1 = "#DD3344", Tumor_plus_NK_timepoint_1 = "#FF9F1C", Tumor_plus_NK_timepoint_2 = "#553388"),
cell_line_label = c( A = "#E9D8A6", B = "#D9BE6D", C = "#676F54", D = "#395B50")
)
heatmap_corrected <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
cluster_cols = FALSE,
cluster_rows = FALSE,
show_rownames = TRUE,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
) # <img src=“/home/rstudio/workspace/nk_tum_immunoediting_files/figure-html/03”try to plot in the big, integrated DATASET”-1.png” width=“672” />
ggsave(filename = paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.eps"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")
ggsave(filename = paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_TpNK_tp2_AB_CD_comparison/", "TpNK_tp2_vs_Tonly_tp1_heatmap_corrected_small_shared_gene_set_padj.png"),
plot=heatmap_corrected,
width = 30, height = 14, units = "cm")Results - EXP9.8 comparing WT vs KO, IFNG and PrfKO_NK timepoints
# Loading the required dependencies:
import::from(.from = variancePartition, fitExtractVarPartModel, sortCols, plotVarPart)
import::from(.from = doParallel, registerDoParallel)
import::from(.from = parallel, makeCluster, stopCluster)
# import utils scripts
import::from(.from = here::here("utils/filterDatasets.R"), "filterDatasets", .character_only=TRUE) # used for filtering
import::from(.from = here::here("utils/rnaSelectTopVarGenes.R"), "rnaSelectTopVarGenes", .character_only=TRUE)
import::from(.from = here::here("utils/edaFunctions.R"), "varPartitionEstimate", "generatePCA", "pcaExtractVariance", "pcaPlotVariance", "pcaCorrPCs", "pcaCorrPCsPlot", .character_only=TRUE)
import::from(.from = here::here("utils/generateResults.R"), "generateResults_upd", .character_only=TRUE) #
import::from(.from = here::here("utils/plotDegResults.R"), "plotVolcano","plotVolcano_repel", .character_only=TRUE)
import::from(.from = here::here("utils/generateEnsemblAnnotation.R"), "generateEnsemblAnnotation", .character_only=TRUE) # used for filtering#project setup
# loading config file ----
config_file <- file.path( "nk_tum_immunoediting_config.yaml") # params$config_file; params not found? ; change to project_config.yaml
config <- yaml::read_yaml(config_file)
#config <- yaml::read_yaml(params$config_file)
experiment_name="nk_tum_immunoedit_complete_ab2" # this can be a subset analysis - e.g. just batch1,...
experiment_design="1" # used only to construct initial dds object
abs_filt_samples=3
# parameters for annotation
biomart_host = "http://nov2020.archive.ensembl.org"
biomart_Ens_version = "Ensembl Genes 102"
biomart_dataset="mmusculus_gene_ensembl"
# directories
output_dir = paste0("./results_dir", "/",experiment_name,"/")
dir.create(output_dir)
# importing only key functions that are actually used - not to polute namespace!
import::from(.from = readr, read_csv, cols)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup) # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = GenomicFeatures, makeTxDbFromGFF)
import::from(.from = AnnotationDbi, annot_db_keys = keys, annot_db_select = select)
import::from(.from = tximport, tximport)
import::from(.from = DESeq2, .all=TRUE)
#table(metadata$condition_tp, metadata$experiment)# Create a list of comparisons:
# Full list
# list_of_comparisons <- list(c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_2"),
# c("Tumor_plus_IFNg_timepoint_2", "Tumor_only_timepoint_2"),
# c("Tumor_plus_PrfKO_NK_timepoint_2", "Tumor_only_timepoint_2"),
# c("Tumor_plus_WT_NK_timepoint_2", "Tumor_plus_IFNg_timepoint_2"),
# c("Tumor_plus_WT_NK_timepoint_2", "Tumor_plus_PrfKO_NK_timepoint_2"),
# c("Tumor_only_timepoint_0", "Tumor_only_timepoint_1"),
# c("Tumor_only_timepoint_0", "Tumor_only_timepoint_2"),
# c("Tumor_only_timepoint_1", "Tumor_only_timepoint_2"),
# c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_1"))
list_of_comparisons <- list(c("Tumor_plus_WT_NK_timepoint_2", "Tumor_only_timepoint_2"),
c("Tumor_plus_IFNg_timepoint_2", "Tumor_only_timepoint_2"),
c("Tumor_plus_PrfKO_NK_timepoint_2", "Tumor_only_timepoint_2"))
experiment_name="nk_tum_immunoedit_complete"
output_dir <- "/home/rstudio/workspace/results_dir/nk_tum_immunoedit_complete/"
# Loading the data
# raw and filtered dds
precomputed_objects_filename <- paste0(experiment_name, "_dds_objects.RData") #"nk_tum_immunoedit_complete_dds_objects.RData"
precomputed_objects_file <- file.path(output_dir, precomputed_objects_filename)
# log2 and vsd transformed
precomputed_transf_objects_filename <- "nk_tum_immunoedit_complete_log2_vsd_filt_objects.RData"
precomputed_transf_objects_file <- file.path(output_dir, precomputed_transf_objects_filename)
# check if object loaded if not load
if(!exists(precomputed_objects_file)){
load(precomputed_objects_file)
load(precomputed_transf_objects_file)
}
# defining parameters
abs_filt_samples=3
padj_cutoff = 0.05
log2FC_cutoff = 0.58 #(FC=1.5); log2FC=1.0 # (FC=2)
var_expl_needed <- 0.6 # at least 60% variance explained needed
experiment_subset <- c("EXP9.8") #we use only this experiment
if(exists("deg_design")) {rm(deg_design)}
deg_design <- as.formula("~ cell_line_label + condition_tp") #Experiment is missing here, since we have only EXP9.8
# Loading MsigDB geneset collections ----
gs_hallmark <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("H"), clean=TRUE)
gs_C2_kegg <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C2"), subcategory = "CP:KEGG", clean=TRUE)
gs_C5_GOBP <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:BP", clean=TRUE)
gs_C5_GOCC <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:CC", clean=TRUE)
gs_C5_GOMF <- hypeR::msigdb_gsets(species = "Mus musculus", category = c("C5"), subcategory = "GO:MF", clean=TRUE)
genes_of_interest <- list("Denn2b"="ENSMUSG00000031024",
"Gbp6"="ENSMUSG00000104713",
"H2-K1"="ENSMUSG00000061232",
"Hs3st2"="ENSMUSG00000046321",
"Htra3"="ENSMUSG00000029096",
"Ifi27"="ENSMUSG00000064215",
"Igtp"="ENSMUSG00000078853",
"Irf1"="ENSMUSG00000018899",
"Lgals3bp"="ENSMUSG00000033880",
"Ly6a"="ENSMUSG00000075602",
"Plaat3"="ENSMUSG00000060675",
"Rtp4"="ENSMUSG00000033355",
"Serpina3f"="ENSMUSG00000066363",
"Stat1"="ENSMUSG00000026104")
genes_of_interest_exp9.8 <- list("H2-Eb1" = "ENSMUSG00000060586",
"Slc16a3"= "ENSMUSG00000025161",
"H2-Aa" = "ENSMUSG00000036594",
"Bnip3" = "ENSMUSG00000078566",
"Il1r2" = "ENSMUSG00000026073",
"Dapk2" = "ENSMUSG00000032380",
"Zfhx3" = "ENSMUSG00000038872",
"Dpp7" = "ENSMUSG00000026958")
#specifically for the publication
genes_of_interest_exp9.4_9.7 <- list('Ifi44' ="ENSMUSG00000028037",
'Ccl5' ="ENSMUSG00000035042",
'Iigp1' ="ENSMUSG00000054072",
'Serpina3f' ="ENSMUSG00000066363",
'Ly6a' ="ENSMUSG00000075602",
'Plaat3' ="ENSMUSG00000060675",
'Lgals3bp' ="ENSMUSG00000033880",
'Ifi27' ="ENSMUSG00000064215",
'Gbp6' ="ENSMUSG00000104713",
'Rtp4' ="ENSMUSG00000033355",
'Stat1' ="ENSMUSG00000026104",
'Tap1' ="ENSMUSG00000037321",
'H2-K1' ="ENSMUSG00000061232",
'B2m' ="ENSMUSG00000060802",
'Igtp' ="ENSMUSG00000078853",
'Gtf2ird1' ="ENSMUSG00000023079",
'Cdc42ep4' ="ENSMUSG00000041598",
'Hoxb6' ="ENSMUSG00000000690",
'Bfsp2' ="ENSMUSG00000032556",
'Sox6' ="ENSMUSG00000051910",
'Tgm2' ="ENSMUSG00000037820",
'Adprhl1' ="ENSMUSG00000031448")# this will be a counter
cid <- 0
#Star of the loop
for (comparison_i in list_of_comparisons){
cid <- cid + 1
#determine the name of the comparison
deg_name <- paste0(cid, comparison_i[1], comparison_i[2]) #"c1_Tumor_plus_WT_NK_timepoint_2_VS_Tumor_only_timepoint_2"
deg_dir <- file.path(output_dir, paste0("deg_", deg_name, "/"))
dir.create(deg_dir)
#this is the tow sample that we are going to compare between
condition_tp_subset <- comparison_i
# preparing dds and vsd objects for the main project comparison
precomputed_objects_deg_filename <- paste0(deg_name, "_dds_objects.RData")
precomputed_objects_deg_file <- file.path(deg_dir, precomputed_objects_deg_filename)
precomputed_transf_objects_deg_filename <- paste0(deg_name, "_log2_vsd_filt_objects.RData")
precomputed_transf_objects_deg_file <- file.path(deg_dir, precomputed_transf_objects_deg_filename)
# just in case remove any existing subsets
if(exists("dds_subset")) {rm(dds_subset)}
if(exists("dds_subset_filt")) {rm(dds_subset_filt)}
if(exists("vsd_subset")) {rm(vsd_subset)}
if(!file.exists(precomputed_objects_deg_file)){
cat("RNA objects file '", precomputed_objects_deg_filename, "' does not exist in the OUTPUT_DIR...\n")
cat("generating", precomputed_objects_deg_filename, "\n")
dds_subset <- dds_filt[ , (dds_filt$condition_tp %in% condition_tp_subset) & (dds_filt$experiment %in% experiment_subset)]
# Dropping levels
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)
dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)
dds_subset$experiment <- droplevels(dds_subset$experiment)
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
# filtering lowly expressed genes
dds_subset_filt <- filterDatasets(dds_subset,
abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size
design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
cat("saving...", precomputed_objects_file, "\n")
cat("...into:", output_dir, "\n")
save(dds_subset, dds_subset_filt, ensemblAnnot, file = precomputed_objects_deg_file)
} else{
cat("RNA objects file '", precomputed_objects_deg_filename, "' exist...loading\n")
load(precomputed_objects_deg_file)
}
#transformation after filtering
if(!file.exists(precomputed_transf_objects_deg_file)){
# transformation
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info; blind = TRUE for QC
save(log2_norm_subset_filt, vsd_subset_filt,
file = precomputed_transf_objects_deg_file)
} else{
load(precomputed_transf_objects_deg_file)
}
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previous runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate)
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)),
strata = c("condition_tp"),
data = key_metadata_subset)
tableone::kableone(key_variables_tableOne$CatTable,
caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
kableExtra::kable_material(c("striped", "hover"))
transf_object <- vsd_subset_filt
#Before removing Batch Effect
pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object,
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle(paste(comparison_i, "Comparison. Dataset Before Batch Correction"))
#plot(pca_deg_plotCellLabel)
# Remove batch effect (only for the cell_line_label)
transf_batch_NObatch_cell_label <- vsd_subset_filt
transf_batch_NObatch_cell_label_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_cell_label),
transf_batch_NObatch_cell_label$cell_line_label)
SummarizedExperiment::assay(transf_batch_NObatch_cell_label) <- transf_batch_NObatch_cell_label_count
pca_deg_NObatch_cell_label <- generatePCA(transf_object = transf_batch_NObatch_cell_label,
cond_interest_varPart = c("condition_tp", "cell_line_label"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle(paste(comparison_i, "Comparison. Removed batchEffect - cell_line"))
#plot(pca_deg_NObatch_cell_label)
PCA_batch_highlightCellLines_merged <- ggpubr::ggarrange(pca_deg_plotCellLabel,
pca_deg_NObatch_cell_label,
common.legend = TRUE)
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "batch_correction.png"),
plot=PCA_batch_highlightCellLines_merged,
width = 40, height = 20, units = "cm")
#Generate results
deg_comparison_results <- generateResults_upd(dds_object = dds_subset_filt,
coeff_name = resultsNames(dds_subset_filt)[3],
cond_numerator = comparison_i[1],
cond_denominator = comparison_i[2],
cond_variable="condition_tp")
#export it as xlsx table
openxlsx::write.xlsx(deg_comparison_results, file = file.path(deg_dir, paste0("deg", comparison_i[1], '_vs_', comparison_i[2], "_results.xlsx")))
#generating GOBP GOCC GOMF
deg_comparison_results_enrichment_C5_GOBP <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
genesets = gs_C5_GOBP,
test="hypergeometric",
background=nrow(dds_subset_filt))
deg_comparison_results_enrichment_C5_GOCC <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
genesets = gs_C5_GOCC,
test="hypergeometric",
background=nrow(dds_subset_filt))
deg_comparison_results_enrichment_C5_GOMF <- hypeR::hypeR(signature = deg_comparison_results$results_signif$mgi_symbol,
genesets = gs_C5_GOMF,
test="hypergeometric",
background=nrow(dds_subset_filt))
# hypeR::hyp_show(hyp_obj$data$clA, simple = FALSE)
hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOBP, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOBP.xlsx")))
hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOCC, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOCC.xlsx")))
hypeR::hyp_to_excel(deg_comparison_results_enrichment_C5_GOMF, file_path=file.path(deg_dir, paste0(comparison_i[1], comparison_i[2],"_enrichment_C5_GOMF.xlsx")))
# plotting enrichment results ----
deg_comparison_results_enrichment_C5_GOBP_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOBP, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOBP: ",comparison_i[1], comparison_i[2]))
deg_comparison_results_enrichment_C5_GOCC_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOCC, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOCC: ",comparison_i[1], comparison_i[2]))
deg_comparison_results_enrichment_C5_GOMF_plot <- hypeR::hyp_dots(deg_comparison_results_enrichment_C5_GOMF, merge=TRUE, fdr=0.05, top = 20, abrv=70, val="fdr", title=paste0("GOMF: ",comparison_i[1], comparison_i[2]))
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOBP_plot.png"),
plot=deg_comparison_results_enrichment_C5_GOBP_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOCC_plot.png"),
plot=deg_comparison_results_enrichment_C5_GOCC_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_GOMF_plot.png"),
plot=deg_comparison_results_enrichment_C5_GOMF_plot,
width = 20, height = 20, units = "cm")
# volcano plot
deg_comparison_results_volcano_plot <- plotVolcano_repel(dds_results_obj = deg_comparison_results$results_all,
genes_of_interest = genes_of_interest,
genes_of_interest2 = genes_of_interest_exp9.8,
plot_title = paste0(comparison_i[1], ' vs ', comparison_i[2]),
max_overlaps = 30)
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_volcano_plot.png"),
plot=deg_comparison_results_volcano_plot,
width = 20, height = 20, units = "cm")
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_volcano_plot.eps"),
plot=deg_comparison_results_volcano_plot,
width = 20, height = 20, units = "cm")
print(deg_comparison_results_volcano_plot)
# heatmap and heatmap batch corrected - corrected
save_pheatmap_pdf <- function(x, filename, width=7, height=7) {
stopifnot(!missing(x))
stopifnot(!missing(filename))
pdf(filename, width=width, height=height)
grid::grid.newpage()
grid::grid.draw(x$gtable)
dev.off()
}
metadata_heatmap <- as.data.frame(colData(dds_subset))
heatmap_counts <- SummarizedExperiment::assay(transf_object)
heatmap_counts_NObatch <- SummarizedExperiment::assay(transf_batch_NObatch_cell_label) # removed xperiment batch effects! ? use experimen+cell_line removed???
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% deg_comparison_results$results_signif$ensembl_id, ]
heatmap_counts_NObatch_deg <- heatmap_counts_NObatch[rownames(heatmap_counts_NObatch) %in% deg_comparison_results$results_signif$ensembl_id, ]
annotation_col <- metadata_heatmap %>%
dplyr::select(cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_NObatch_deg_ord <- heatmap_counts_NObatch_deg[, match(rownames(annotation_col), colnames(heatmap_counts_NObatch_deg))]
ensembl2symbol_annot <- deg_comparison_results$results_all %>%
dplyr::select(ensembl_id, mgi_symbol)
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.8 = "grey")
)
# , filename = paste0(deg_TpNK_tp2_vs_Tonly_tp1_DIR, "tp2_vs_tp1_inclEXP9_7_heatmap_intersect_54genes.pdf")
heatmap_result <- pheatmap::pheatmap(heatmap_counts_NObatch_deg_ord,
main = "Heatmap of signif. DEG - removed cell line BatchEffect",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
)
save_pheatmap_pdf(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap_batch_eff_removed.pdf"),
x=heatmap_result,
width = 10, height = 10)
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap_batch_eff_removed.png"),
plot=heatmap_result,
width = 10, height = 10, units = "in")
#Heatmap not corrected for the batch effect
heatmap_result <- pheatmap::pheatmap(heatmap_counts_deg_ord,
main = "Heatmap of signif. DEG",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = FALSE,
cluster_cols = FALSE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
)
save_pheatmap_pdf(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap.pdf"),
x=heatmap_result,
width = 10, height = 10)
ggsave(filename = paste0(deg_dir, comparison_i[1], "_VS_",comparison_i[2], "_heatmap.png"),
plot=heatmap_result,
width = 10, height = 10, units = "in")
} ## RNA objects file ' 1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading
## RNA objects file ' 2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading
## RNA objects file ' 3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2_dds_objects.RData ' exist...loading
Plotting the Venn Diagramm for the comparisons:
| Deg | Group 1 | Group 2 |
| — | ——————————- | ——————————- |
| 1 | Tumor_plus_WT_NK_timepoint_2 | Tumor_only_timepoint_2 |
| 2 | Tumor_plus_IFNg_timepoint_2 | Tumor_only_timepoint_2 |
| 3 | Tumor_plus_PrfKO_NK_timepoint_2 | Tumor_only_timepoint_2 |
Deg1_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_WT_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg2_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2/degTumor_plus_IFNg_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg3_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_PrfKO_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg1 <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_ens <- Deg1[,c('mgi_symbol', 'ensembl_id')]
Deg1 <- Deg1$mgi_symbol
Deg2 <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_ens <- Deg2[, c('mgi_symbol', 'ensembl_id')]
Deg2 <- Deg2$mgi_symbol
Deg3 <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_ens <- Deg3[, c('mgi_symbol', 'ensembl_id')]
Deg3 <- Deg3$mgi_symbol
Deg_list <- list("Tmr+WT_NK TP2 vs T only TP2" = Deg1,
"Tmr+IFNg TP2 vs T only TP2" = Deg2,
"Tmr+PrfKO NK TP2 vs T only TP2" = Deg3)
library(ggvenn)
ven_diag <- ggvenn(Deg_list)
ven_diag
#order the genes for the further Heatmap
Deg12_intersect <- intersect(Deg1, Deg2)
Deg123_intersect <- intersect(Deg12_intersect, Deg3)
Deg23_intersect <- intersect(Deg2, Deg3)
Deg31_intersect <- intersect(Deg3, Deg1)
Deg12_intersect <- Deg12_intersect[!(Deg12_intersect %in% Deg123_intersect)]
Deg23_intersect <- Deg23_intersect[!(Deg23_intersect %in% Deg123_intersect)]
Deg31_intersect <- Deg31_intersect[!(Deg31_intersect %in% Deg123_intersect)]
Deg1_unique <- Deg1[!(Deg1 %in% Deg12_intersect |
Deg1 %in% Deg31_intersect |
Deg1 %in% Deg123_intersect)]
Deg2_unique <- Deg2[!(Deg2 %in% Deg12_intersect |
Deg2 %in% Deg23_intersect |
Deg2 %in% Deg123_intersect)]
Deg3_unique <- Deg3[!(Deg3 %in% Deg31_intersect |
Deg3 %in% Deg23_intersect |
Deg3 %in% Deg123_intersect)]
List_of_genes_ordered <- c(Deg31_intersect,
Deg1_unique,
Deg12_intersect,
Deg2_unique,
Deg23_intersect,
Deg3_unique,
Deg123_intersect)
Deg_ens <- rbind(Deg1_ens, Deg2_ens, Deg3_ens)
Deg_ens <- unique(Deg_ens)
Deg_ens_ord <- Deg_ens[match(List_of_genes_ordered, Deg_ens$mgi_symbol),]#These are the data that will be used for the heatmap
list_of_comparisons_venn_heatmap <- c("Tumor_plus_WT_NK_timepoint_2",
"Tumor_plus_IFNg_timepoint_2",
"Tumor_plus_PrfKO_NK_timepoint_2",
"Tumor_only_timepoint_2")
experiment_subset <- c("EXP9.8")
dds_subset_venn_heatmap <- dds_filt[ , (dds_filt$condition_tp %in% list_of_comparisons_venn_heatmap) & (dds_filt$experiment %in% experiment_subset)]
metadata_heatmap <- as.data.frame(colData(dds_subset_venn_heatmap))
heatmap_counts <- SummarizedExperiment::assay(dds_subset_venn_heatmap)
heatmap_counts_deg <- heatmap_counts[rownames(heatmap_counts) %in% Deg_ens_ord$ensembl_id,]
annotation_col <- metadata_heatmap %>%
dplyr::select(cell_line_label, condition_tp) %>% # condition, timepoint_cell_harvesting,
dplyr::arrange(condition_tp, cell_line_label)
heatmap_counts_deg_ord <- heatmap_counts_deg[, match(rownames(annotation_col), colnames(heatmap_counts_deg))]
heatmap_counts_deg_ord <- heatmap_counts_deg_ord[match(Deg_ens_ord$ensembl_id, rownames(heatmap_counts_deg_ord)),]
rownames(heatmap_counts_deg_ord) == Deg_ens_ord$ensembl_id## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [27] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [53] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [79] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [105] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [131] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [209] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [235] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [261] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [287] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [339] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [365] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [417] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [443] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [469] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [495] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [521] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [547] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [573] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [599] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [625] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [651] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [677] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [703] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [729] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUErownames(heatmap_counts_deg_ord) <- Deg_ens_ord$mgi_symbol
color.scheme <- rev(RColorBrewer::brewer.pal(8,"RdBu")) # generate the color scheme to use
ann_colors = list(
experiment = c(EXP9.4 = "#4682B4", EXP9.5 = "#7846B4", EXP9.6 = "#B47846", EXP9.7 = "#82B446", EXP9.8 = "grey")
)
heatmap_result <- pheatmap::pheatmap(heatmap_counts_deg_ord,
main = "Heatmap of signif. DEG",
scale = "row",
annotation_col = annotation_col,
annotation_colors = ann_colors,
#annotation_row = row_annot_symbols,
show_colnames = FALSE,
show_rownames = TRUE,
cluster_cols = FALSE,
cluster_rows = T,
annotation_names_row = TRUE,
#cluster_rows = counts_deg_ord_row_cor_hclust,
color = color.scheme,
fontsize = 10, fontsize_row = 10 #height=10, cellwidth = 11, cellheight = 11
)
# This heatmap doesn't look so great.
# save the DEG list:
List_of_genes_ordered_forxlsx <- list("Deg1_T+WT_NK_TP2_VS_To_TP2" = Deg1,
"Deg2_T+IFNg_TP2_VS_To_TP2" = Deg2,
"Deg3_T+PrfKO_NK_TP2_VS_To_TP2" = Deg3,
"Deg31_exlusive_intersect" = Deg31_intersect,
"Deg1_unique" = Deg1_unique,
"Deg12_exlusive_intersect" = Deg12_intersect,
"Deg2_unique" = Deg2_unique,
"Deg23_exlusive_intersect" = Deg23_intersect,
"Deg3_unique" = Deg3_unique,
"Deg123_intersect" = Deg123_intersect)
openxlsx::write.xlsx(List_of_genes_ordered_forxlsx,
"~/workspace/results_dir/nk_tum_immunoedit_complete/overlaping_genes_deg_1_2_3.xlsx")
# adding supplementary information (p-val, L2FC, etc for genes)
# read the DEG files again
Deg1_xlsx <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_xlsx <- Deg1_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
Deg2_xlsx <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_xlsx <- Deg2_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
Deg3_xlsx <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_xlsx <- Deg3_xlsx %>% select("ensembl_id", "mgi_symbol", "mgi_description", "log2FoldChange", "padj")
List_of_genes_ordered_forxlsx_sup <- List_of_genes_ordered_forxlsx
List_of_genes_ordered_forxlsx_sup$`Deg1_T+WT_NK_TP2_VS_To_TP2` <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg1_T+WT_NK_TP2_VS_To_TP2`, Deg1_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$`Deg2_T+IFNg_TP2_VS_To_TP2` <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg2_T+IFNg_TP2_VS_To_TP2`, Deg2_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$`Deg3_T+PrfKO_NK_TP2_VS_To_TP2` <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$`Deg3_T+PrfKO_NK_TP2_VS_To_TP2`, Deg3_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$Deg1_unique <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg1_unique, Deg1_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$Deg2_unique <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg2_unique, Deg2_xlsx$mgi_symbol),]
List_of_genes_ordered_forxlsx_sup$Deg3_unique <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg3_unique, Deg3_xlsx$mgi_symbol),]
Deg31_excl <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect, Deg3_xlsx$mgi_symbol),] %>%
rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")
Deg13_excl <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect, Deg1_xlsx$mgi_symbol),] %>%
select("log2FoldChange", "padj") %>%
rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg31_exlusive_intersect <- cbind(Deg31_excl, Deg13_excl)
Deg21_excl <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect, Deg2_xlsx$mgi_symbol),] %>%
rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")
Deg12_excl <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect, Deg1_xlsx$mgi_symbol),] %>%
select("log2FoldChange", "padj") %>%
rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect <- cbind(Deg21_excl, Deg12_excl)
Deg23_excl <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg23_exlusive_intersect, Deg2_xlsx$mgi_symbol),] %>%
rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")
Deg32_excl <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg23_exlusive_intersect, Deg3_xlsx$mgi_symbol),] %>%
select("log2FoldChange", "padj") %>%
rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg12_exlusive_intersect <- cbind(Deg23_excl, Deg32_excl)
Deg123_1 <- Deg1_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg1_xlsx$mgi_symbol),] %>%
rename("DEG1_log2FoldChange" = "log2FoldChange", "DEG1_padj" = "padj")
Deg123_2 <- Deg2_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg2_xlsx$mgi_symbol),] %>%
select("log2FoldChange", "padj") %>%
rename("DEG2_log2FoldChange" = "log2FoldChange", "DEG2_padj" = "padj")
Deg123_3 <- Deg3_xlsx[match(List_of_genes_ordered_forxlsx_sup$Deg123_intersect, Deg3_xlsx$mgi_symbol),] %>%
select("log2FoldChange", "padj") %>%
rename("DEG3_log2FoldChange" = "log2FoldChange", "DEG3_padj" = "padj")
List_of_genes_ordered_forxlsx_sup$Deg123_intersect <- cbind(Deg123_1, Deg123_2, Deg123_3)
openxlsx::write.xlsx(List_of_genes_ordered_forxlsx_sup,
"~/workspace/results_dir/nk_tum_immunoedit_complete/overlaping_genes_deg_1_2_3_with_extra.xlsx")Plots for the publication:
Plotting the PCA plot of the 4 groups
#Tumor_plus_WT_NK_timepoint_2 Tumor_plus_IFNg_timepoint_2 Tumor_plus_PrfKO_NK_timepoint_2 Tumor_only_timepoint_2
save_dir <- "~/workspace/results_dir/EXP9.8_publication/"
dir.create(save_dir)
condition_tp_subset <- c("Tumor_plus_WT_NK_timepoint_2",
"Tumor_plus_IFNg_timepoint_2",
"Tumor_plus_PrfKO_NK_timepoint_2",
"Tumor_only_timepoint_2")
dds_subset <- dds_filt[ , (dds_filt$condition_tp %in% condition_tp_subset) & (dds_filt$experiment %in% experiment_subset)]
# Dropping levels
dds_subset$condition_tp <- droplevels(dds_subset$condition_tp)
table(dds_subset$condition_tp)##
## Tumor_only_timepoint_2 Tumor_plus_WT_NK_timepoint_2 Tumor_plus_IFNg_timepoint_2 Tumor_plus_PrfKO_NK_timepoint_2
## 6 6 6 6dds_subset$experiment <- droplevels(dds_subset$experiment)
table(dds_subset$experiment)##
## EXP9.8
## 24dds_subset$experiment <- droplevels(dds_subset$experiment)
dds_subset$cell_line_label <- droplevels(dds_subset$cell_line_label)
# filtering lowly expressed genes
dds_subset_filt <- filterDatasets(dds_subset,
abs_filt = TRUE,
abs_filt_samples = abs_filt_samples) # at least in N samples, which is a smallest group size## Original dds object samples: 24 genes: 15430
## Minimum number of samples with expression: 3
## Number of filtered genes: 3228
## Filtered dds object has samples: 24 genes: 12202design(dds_subset_filt) <- deg_design
dds_subset_filt <- DESeq2::estimateSizeFactors(dds_subset_filt)
dds_subset_filt <- DESeq2::DESeq(dds_subset_filt) # do not replace outliers based on replicates
log2_norm_subset_filt <- DESeq2::normTransform(dds_subset_filt)
vsd_subset_filt <- DESeq2::vst(dds_subset_filt, blind = FALSE) # using blind=FALSE utilize design info;
rm(key_metadata_subset, key_variables_tableOne) # in case this exists from previous runs
key_metadata_subset <- as.data.frame(colData(dds_subset)) %>%
dplyr::select(experiment, condition_tp, cell_line_label, technical_replicate)
key_variables_tableOne <- tableone::CreateTableOne(vars = colnames(dplyr::select(key_metadata_subset, -condition_tp)),
strata = c("condition_tp"),
data = key_metadata_subset)
tableone::kableone(key_variables_tableOne$CatTable,
caption = "Overview of number of samples in different categories (experiment, condition,...).") %>%
kableExtra::kable_material(c("striped", "hover"))| Tumor_only_timepoint_2 | Tumor_plus_WT_NK_timepoint_2 | Tumor_plus_IFNg_timepoint_2 | Tumor_plus_PrfKO_NK_timepoint_2 | p | test | |
|---|---|---|---|---|---|---|
| n | 6 | 6 | 6 | 6 | ||
| experiment = EXP9.8 (%) | 6 (100.0) | 6 (100.0) | 6 (100.0) | 6 (100.0) | NA | |
| cell_line_label = D (%) | 3 ( 50.0) | 3 ( 50.0) | 3 ( 50.0) | 3 ( 50.0) | 1.000 | |
| technical_replicate (%) | 1.000 | |||||
| 1 | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) | ||
| 2 | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) | ||
| 3 | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) | 2 ( 33.3) |
transf_object <- vsd_subset_filt
#Before removing Batch Effect
pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object[,transf_object$cell_line_label == "A"],
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle(paste( "PCA plot. EXP9.8 Cell line A")) +
scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))
plot(pca_deg_plotCellLabel)
ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_A.png"), pca_deg_plotCellLabel,
width = 20, height = 14, units = "cm")
pca_deg_plotCellLabel <- generatePCA(transf_object = transf_object[,transf_object$cell_line_label == "D"],
cond_interest_varPart = c("condition_tp", "cell_line_label", "experiment"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
ggtitle(paste( "PCA plot. EXP9.8 Cell line D")) +
scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))+
scale_shape_manual(values = 17)
plot(pca_deg_plotCellLabel)
ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_D.png"), pca_deg_plotCellLabel,
width = 20, height = 14, units = "cm")
# Remove batch effect (only for the cell_line_label)
transf_batch_NObatch_cell_label <- vsd_subset_filt
transf_batch_NObatch_cell_label_count <- limma::removeBatchEffect(SummarizedExperiment::assay(transf_batch_NObatch_cell_label),
transf_batch_NObatch_cell_label$cell_line_label)
SummarizedExperiment::assay(transf_batch_NObatch_cell_label) <- transf_batch_NObatch_cell_label_count
pca_deg_NObatch_cell_label <- generatePCA(transf_object = transf_batch_NObatch_cell_label,
cond_interest_varPart = c("condition_tp", "cell_line_label"),
color_variable = "condition_tp",
shape_variable = "cell_line_label",
ntop_genes = 1000) +
scale_color_manual(values = c("#DD3344" ,"#553388", "#A3E7FC", "#26C485"))+
ggtitle(paste( "PCA plot. EXP9.8 Batch corrected for cell_line"))
plot(pca_deg_NObatch_cell_label)
ggsave(filename = file.path(save_dir, "PCA_Plot_all_conditions_batch_corrected_cell_lines.png"), pca_deg_NObatch_cell_label,
width = 20, height = 14, units = "cm")
##### Venn diagramms
Deg1_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_1Tumor_plus_WT_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_WT_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg2_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_2Tumor_plus_IFNg_timepoint_2Tumor_only_timepoint_2/degTumor_plus_IFNg_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg3_filename <- paste0("~/workspace/results_dir/nk_tum_immunoedit_complete/deg_3Tumor_plus_PrfKO_NK_timepoint_2Tumor_only_timepoint_2/degTumor_plus_PrfKO_NK_timepoint_2_vs_Tumor_only_timepoint_2_results.xlsx")
Deg1 <- openxlsx::read.xlsx(Deg1_filename,sheet = 'results_signif')
Deg1_pos <- Deg1[Deg1$log2FoldChange > 0, c('mgi_symbol') ]
Deg1_neg <- Deg1[Deg1$log2FoldChange < 0, c('mgi_symbol') ]
Deg2 <- openxlsx::read.xlsx(Deg2_filename,sheet = 'results_signif')
Deg2_pos <- Deg2[Deg2$log2FoldChange > 0, c('mgi_symbol') ]
Deg2_neg <- Deg2[Deg2$log2FoldChange < 0, c('mgi_symbol') ]
Deg2_neg <- Deg2_neg[Deg2_neg!=""]
Deg3 <- openxlsx::read.xlsx(Deg3_filename,sheet = 'results_signif')
Deg3_pos <- Deg3[Deg3$log2FoldChange > 0, c('mgi_symbol') ]
Deg3_neg <- Deg3[Deg3$log2FoldChange < 0, c('mgi_symbol') ]
Deg3_neg <- Deg3_neg[Deg3_neg!=""]
Deg_list_pos <- list("Tmr+WT_NK TP2 vs T only TP2" = Deg1_pos,
"Tmr+IFNg TP2 vs T only TP2" = Deg2_pos,
"Tmr+PrfKO NK TP2 vs T only TP2" = Deg3_pos)
Deg_list_neg <- list("Tmr+WT_NK TP2 vs T only TP2" = Deg1_neg,
"Tmr+IFNg TP2 vs T only TP2" = Deg2_neg,
"Tmr+PrfKO NK TP2 vs T only TP2" = Deg3_neg)
library(ggvenn)
ven_diag <- ggvenn(Deg_list_pos,fill_color = c("#553388", "#A3E7FC", "#26C485"))
ven_diag
ggsave(filename = file.path(save_dir, "venn_diag_positive_up_reg.png"), ven_diag,
width = 20, height = 20, units = "cm")
ven_diag <- ggvenn(Deg_list_neg,fill_color = c("#553388", "#A3E7FC", "#26C485"))
ven_diag
ggsave(filename = file.path(save_dir, "venn_diag_positive_down_reg.png"), ven_diag,
width = 20, height = 20, units = "cm")
Deg12_intersect_pos <- intersect(Deg1_pos, Deg2_pos)
Deg123_intersect_pos <- intersect(Deg12_intersect_pos, Deg3_pos)
Deg23_intersect_pos <- intersect(Deg2_pos, Deg3_pos)
Deg31_intersect_pos <- intersect(Deg3_pos, Deg1_pos)
Deg12_intersect_pos_scecif <- setdiff(Deg12_intersect_pos, Deg123_intersect_pos)
Deg23_intersect_pos_specif <- setdiff(Deg23_intersect_pos, Deg123_intersect_pos)
Deg31_intersect_pos_specif <- setdiff(Deg31_intersect_pos, Deg123_intersect_pos)
setdiff(Deg1_pos, c(Deg2_pos, Deg3_pos))## [1] "Bnip3" "Aldoa" "Pgk1" "Tpi1" "Ero1l" "Pkm" "Ddit4" "Pdk1" "Bnip3l" "Pgm1" "Fam162a" "Ulk3" "Pfkl"
## [14] "Rasal3" "Pfkp" "Ankrd37" "Kdm3a" "Map4" "Slc37a4" "Tgm2" "Trpc4" "Zfp810" "Cpne5" "Cox7a1" "Egln1" "Ak4"
## [27] "Aldoc" "Hsd11b1" "Zfp62" "Crlf1" "Mgarp" "Ropn1l" "Cd244a" "Gstm4" "Ercc1" "Izumo4"setdiff(Deg2_pos, c(Deg1_pos, Deg3_pos))## [1] "Ifi47" "Tgtp2" "Gbp7" "Gbp4" "Gbp5" "Irgm2" "Tap1" "H2-K1"
## [9] "Jun" "Socs1" "Serpina3f" "Gm12250" "Irgm1" "B2m" "Tapbpl" "Batf2"
## [17] "Cd1d1" "9330175E14Rik" "F830016B08Rik" "Psmb8" "Serpina3g" "Gbp3" "Parp11" "Gm12216"
## [25] "Irf9" "AW112010" "Psmb10" "Ppa1" "Nlrc5" "Wars" "Lap3" "Ifitm3"
## [33] "Gm43302" "Parp9" "Dgcr6" "Il18bp" "Ube2l6" "Trafd1" "Tapbp" "Tmem140"
## [41] "Psap" "Nampt" "Casp1" "Trim12c" "Tfrc" "Rnf19b" "Gbp2b" "Ly6c1"
## [49] "Isg20" "Ctss" "Coa5" "Gpx1" "Slfn2" "Ifi44" "Psme1" "Dtx3l"
## [57] "Psmg4" "Bst2" "Gimap4" "P2ry14" "Ifitm1" "Isg15" "Tnfrsf26" "Ffar2"
## [65] "Prm1" "H2-D1" "Sp110" "Gm1966" "Psme2" "Trim30a" "Idnk" "Mpeg1"
## [73] "Pecam1" "Ifngr2" "Ccr2" "Tnfsf10" "Prodh" "Gm4486" "Ifi35" "Itm2b"
## [81] "Csf1" "Psmb9" "Samhd1" "Usp18" "Ogfr" "Fas" "Ifi209" "Rnf114"
## [89] "Ly6c2" "Oasl2" "Snx10" "Cd274" "Cfp" "Rsad2" "Plac8" "Psme2b"
## [97] "Guca1a" "Klf6" "Tmem33" "Erap1" "H2-T22" "Ctsc" "Aida" "Cask"
## [105] "Ms4a6c" "Myof" "Rmdn3" "BC051226" "Cemip2" "Nod1" "Gm12840" "Xaf1"
## [113] "H2-Q5" "Tgtp1" "Phc2" "Parp12" "Reps1" "Oas3" "Mapk9" "Rnase6"
## [121] "Glrx" "Vwa5a" "Il2rg" "Lgals3" "Ppp2r5b" "1600014C10Rik" "Ly6d" "Rsbn1l"
## [129] "Apol7b" "Nuak2" "Dio2" "Ciita" "Olfr753-ps1" "Fcgr3" "Nmi" "Snhg15"
## [137] "Tmtc4" "Apex2" "Trim34a" "Dkk3" "Gm20627" "Ica1" "Prdm1" "Serpinb6a"
## [145] "Ifit1" "Cebpb" "Tmco4" "Cep89" "Ccl9" "Stat3" "Gm6545" "Plscr1"
## [153] "Mmp13" "Sema3g" "H2-DMb2" "Piwil2" "Uba7" "Ccrl2" "Chpf2" "Rasa4"
## [161] "Gda" "Arhgap25" "Lax1" "Ifit1bl1" "Parp14" "Irf7" "Gm49339" "Selenow"
## [169] "Tusc1" "Ryr2" "Atp8a1" "Sco1" "Gm48161" "Fbn1" "Rgs1" "Pon3"
## [177] "Marchf5" "Znrd2" "Srxn1" "Tspan2" "Gpr18" "Tmem50b" "Adar" "Gm50255"
## [185] "Osm" "Phf11a" "Gm10076" "Shb" "Il12rb1" "Trim12a" "H2-T23" "Igf1"
## [193] "Pgf" "Wsb2" "Ifi203" "Ifi213" "Scly" "Usf1" "Bcl3" "Alcam"
## [201] "Sh2d5" "Timm21" "Cd36" "Vsir" "Snhg5" "Cmpk2" "Minpp1" "Tmem138"
## [209] "Traf5" "Creb3" "Smg8" "Mat2b" "Sesn3" "Il15ra" "Ly6i" "Rpl17-ps8"
## [217] "Ifi206" "Themis2" "Tmem86a" "Parp10" "Gpatch11" "Zbtb32" "Oas1a" "Zfp422"
## [225] "Serpini1" "Dhx58" "Cxcr3" "BC147527" "Ifi211" "Ndufc1" "Hck" "Gm16701"
## [233] "Slc11a1" "Prkaa2" "Lsm8" "Aopep" "Snx14" "Inpp4a" "Hpgds" "Calhm6"
## [241] "Zfp846" "Rnf213" "Trim21" "Gpm6a" "Slc35e2" "Ssbp2" "Abtb2" "Ssbp1"
## [249] "Marchf1" "Mtf1" "Rps18-ps6" "H2-T24" "Bpnt1" "Nt5e" "Polr1e" "Coq10b"
## [257] "Cog1" "Rangrf" "Htatip2" "Dnase1l3" "Slc22a5" "Trmt1l" "Slc23a2" "H2-DMb1"
## [265] "Parp3" "Mettl17" "Lyrm1" "Igkc" "Rhbdf2" "Slc30a4" "9930104L06Rik" "Ccdc146"
## [273] "Gosr1" "Tmem106a" "Abhd15" "Cpne8" "Gm24265" "Erlin1" "Endod1" "Vps51"
## [281] "Nfkb1" "Aicda" "Slc26a6"setdiff(Deg3_pos, c(Deg1_pos, Deg2_pos))## [1] "Rnaseh2a" "Dmrta2" "Tstd3" "Fastkd2" "Rad52" "Cox6a2" "Dpf1" "Tnnt2" "Abcb4" "Cd28" "Pmel" "Ltk"
## [13] "Zfp113" "Poli" "H2bc3" "Mapk12"Deg12_intersect_neg <- intersect(Deg1_neg, Deg2_neg)
Deg123_intersect_neg <- intersect(Deg12_intersect_neg, Deg3_neg)
Deg23_intersect_neg <- intersect(Deg2_neg, Deg3_neg)
Deg31_intersect_neg <- intersect(Deg3_neg, Deg1_neg)
Deg12_intersect_neg_scecif <- setdiff(Deg12_intersect_neg, Deg123_intersect_neg)
Deg23_intersect_neg_specif <- setdiff(Deg23_intersect_neg, Deg123_intersect_neg)
Deg31_intersect_neg_specif <- setdiff(Deg31_intersect_neg, Deg123_intersect_neg)
setdiff(Deg1_neg, c(Deg2_neg, Deg3_neg))## [1] "Phip" "Ighg2b" "Zfp709" "Tlk2" "H2ac20" "Lbhd1" "Pogk" "Gm20186"
## [9] "Rgs10" "1300002E11Rik" "R3hdm2" "Gng4" "Rabgap1l" "Aicda" "Smcr8" "Tcf7l1"
## [17] "Ggps1" "Stag2" "2700038G22Rik" "Marcks" "Smpd3" "Gas7" "4930523C07Rik" "Samd8"
## [25] "Ago1" "Ik" "Rpph1"setdiff(Deg2_neg, c(Deg1_neg, Deg3_neg))## [1] "Cdh1" "Ckap4" "St3gal5" "Zcchc24" "Txnrd3" "Neurl1b" "Pygm" "Dipk1b"
## [9] "Cd22" "Grb7" "Crhbp" "Prkcb" "Mki67" "Lta" "Strbp" "Cd248"
## [17] "Actn1" "Klhdc2" "Trp53i11" "Ramp1" "Itih5" "Nsg2" "Mgll" "Mier3"
## [25] "Ndrg1" "Adgrl1" "Tac4" "Nuak1" "Tyro3" "Lat" "Scube3" "Ptgs1"
## [33] "Nedd9" "Coro7" "Otub2" "Kifc5b" "Ptp4a3" "Dgkd" "Tox" "Gm36120"
## [41] "Vangl2" "Cd37" "Endou" "Atad5" "Plcl1" "Epb41l5" "Hs3st2" "Slc16a3"
## [49] "1700019D03Rik" "Ehd2" "Rgs7bp" "Def6" "Pycr1" "Ets1" "Satb1" "Jag2"
## [57] "Napg" "Ctnnbip1" "Irf4" "Faap100" "Loxl2" "Nfia" "Tcn2" "Shroom3"
## [65] "Gas2l3" "Scd1" "Bok" "Kdm7a" "Gm48768" "Irs2" "Pcx" "Cmah"
## [73] "Abi2" "Adgre5" "Gmpr" "Iqsec1" "Pola1" "Hbb-bt" "Card11" "Dusp7"
## [81] "Prdm8" "E2f7" "Pou2f1" "Hvcn1" "Icam2" "1700120C14Rik" "Ano10" "Igf2bp2"
## [89] "Mfsd4a" "Pecr" "Cit" "Emp1" "Egfl7" "Klrb1c" "Tmed6" "Smtnl2"
## [97] "Pik3c2a" "Atp2a3" "Bhlhe40" "Sema4b" "Nab2" "Tcf7l2" "Jcad" "Tbx19"
## [105] "Slc37a2" "Sema5a" "Brdt" "C730034F03Rik" "Nkd2" "C2cd5" "Abca2" "Arhgef1"
## [113] "Gm9227" "S100a11" "Slc47a1" "Kank2" "Ncs1" "BC049352" "Nek1" "Fam160b1"
## [121] "Abraxas1" "Pla2g2f" "Gm42047" "Gdf11" "Batf" "Ccdc47" "Crisp1" "Pygl"
## [129] "Ltb" "0610040J01Rik" "A730063M14Rik" "Stradb" "Cryl1" "Rtn1" "Ephx1" "Cdc42ep4"
## [137] "Accs" "Rbms3" "Slc44a1" "4632427E13Rik" "Rcan3" "St6gal1" "Ccpg1" "Rapgefl1"
## [145] "Tnfrsf9" "Glis2" "Slc17a8" "Bfsp2" "Tmem176b" "Fam8a1" "E130308A19Rik" "Tmem158"
## [153] "Mcm8" "Rapgef3" "Tsc22d3" "Rasl10a" "Chst15" "Syndig1l" "Map3k1" "Srgap3"
## [161] "Stox2" "H2ac6" "Vldlr" "Usp6nl" "Smpd1" "Slc25a40" "Kdm4c" "Abcd3"
## [169] "Mbd4" "Tnni2" "Plin3" "Fhl1" "Azin1" "Prxl2c" "Pcdh11x" "Hes1"
## [177] "Kif21b" "Cth" "Mybl1" "Armt1" "Izumo1r" "Cdhr2" "Mtss1" "Cbx7"
## [185] "Ccdc112" "Zfp821" "Cdhr4" "Smyd4" "Gm9993" "Cradd" "Uaca" "Gm36684"
## [193] "Gpt2" "Hook3" "Acp5" "Wdr91" "Arhgef17" "Gm11824" "Gfi1b" "Hid1"
## [201] "Gm26531" "Bclaf3" "Pcdh9" "Ier3" "Sel1l3" "Abtb1" "Gkap1" "Rtkn2"
## [209] "Sorcs2" "Qrfp" "Tasor2" "Apbb2" "Syngr1" "Rprm" "4933427D14Rik" "Ddx25"
## [217] "Zfp318" "Dalrd3" "Prpf40b" "Mpp3" "Ribc1" "Itga9" "Zyg11b" "Cavin1"
## [225] "Sult1d1" "Abcb4" "Gm867" "Angpt1" "Grip1" "Acy3" "Mpp7" "a"
## [233] "Vps13b" "Cpm" "Txnrd2" "Hs3st1" "Ltbp4" "Tulp4" "Chd3" "Pcbd1"
## [241] "Nme3" "Spats2" "Dusp23" "Slc7a3" "Tctn1" "Zfp820" "Phldb1" "Zfp956"
## [249] "Dync2h1" "Cdkal1" "Six5" "Ccdc88c" "Nup160" "Meis2" "Zan" "Gas2"
## [257] "Ramp2"setdiff(Deg3_neg, c(Deg1_neg, Deg2_neg))## [1] "Utp20" "Snrpd3" "Dleu2" "Rbm19" "Slfn2" "Glb1l" "Lmo7" "Cep290" "Maged1" "Tbk1" "Svip"
## [12] "Rn7sk" "Kmt2d" "Iqce" "Gm47304" "Bmp2k" "Clock" "Eif4enif1" "Rmnd1" "Enc1" "Ap1ar" "Sema7a"
## [23] "Mir17hg" "Relch"cid <- 0
###### volcano plots for the publication ######sessionInfo
sessionInfo()
# saving session info
sink(file.path(results_dir, paste0("sessionInfo_", project_name,".txt")))
sessionInfo()
sink()
renv::snapshot(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))
renv::status(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))---
title: "NK cells immunoediting"
author: "Aleksandr Bykov + code from Peter Repiscak"
date: '`r format(Sys.time(), "%d %B %Y")`'
output:
 html_document:
  theme: readable
  highlight: tango
  code_folding: hide
  code_download: true
#  toc: true
#  toc_depth: 2
#  toc_float: true
always_allow_html: true
params:
  config_file: "nk_tum_immunoediting_config.yaml"
---

# Project description 

Uncovering immune evasion mechanisms of leukemic cells from natural killer cells. In this sub-project RNA sequencing of leukemic cells is used to discover transcriptional changes resulting in NK cell-resistance. There is another sub-project using ATAC-seq (transposase-accessible chromatin) to epigenetic changes resulting in NK cell-resistance - this is not part of this report.

## Experimental design

Taken from discussion summary ('21009_Porject discussion summary.docx'): 

*	4 different cell lines (p185#13G, p185#13H, p185#15M, p185#15O). ADD description about cell line differences.
*	All conditions in technical triplicates and 2 biological replicates (biological replicates can be added any time, if there is a need).
* Technical replicate means that we already start the co-culture in a separate well.

![Biological and technical replicates](NKproject_bio_technical_replicates.png)

## Timeline of the experiment

*	On time point Day 4 we FACS sort the original tumor cells (like Day 0) and the tumor cells which were co-cultured for 4 days with NK cells for the following analyses: ATAC seq, RNA seq and barcode analysis.
Additionally we co-culture a certain amount of the same tumor cells again with NK cells for the analysis of Day 10.
*	On time point Day 10 we FACS sort the tumor cells which were co-cultured for 10 days with NK cells for the same analyses: ATAC seq, RNA seq and barcode analysis.

![Experiment Timepoints](NKproject_experiment_timepoints.png)

# RNA-seq data analysis {.tabset .tabset-pills}

RNA-seq methods: QuantSeq 3' mRNA-Seq - 1x50bp HiSeq 3000/4000. 

Pre-processing: ADD methods+versions for preprocessing
![Pre-processing pipeline](NKproject_preprocessing_pipeline.png)

Downstream analysis: ADD methods+versions for downstream!


[MultiQC report](NKproject_overall_multiqc.html)
ADD full multiqc report description. Few observations:

* 3' bias (expected for Quant-seq)
* EXP9.8 has longer reads (?) 120bp as opposed to 51bp for every other experiment. This is also reflected in a higher alignment to coding region. Further, a clear separation of EXP9.8 from other experiments is observed on the PCA plot. However, also EXP9.7 is somehow separated (along the PC2) from the other experiments. The quantification and alignment were done with the same settings all the experiments


```{r analysis_parameters, include=FALSE}
# Specifying parameters here ----
# remotes::install_github("rstudio/renv@v0.16.0")
if(!("renv" %in% installed.packages())){install.packages("renv", version="0.16.0")}  # docker has 0.15.4; change in production image! 
if(!("import" %in% installed.packages())){renv::install("import")}
if(!("yaml" %in% installed.packages())){renv::install("yaml")}
if(!("knitr" %in% installed.packages())){renv::install("knitr")}

# setting up initial parameters
base_dir <- "/home/rstudio/workspace/"
data_dir <- file.path(base_dir, "datasets") 
dir.create(data_dir)
annotation_dir = paste0(data_dir, "/annotations/")
dir.create(annotation_dir)
results_dir <- file.path(base_dir, "results_dir") 
dir.create(results_dir)
setwd(base_dir)

# project setup
# loading config file ----
config_file <- file.path(base_dir, "nk_tum_immunoediting_config.yaml")  
config <- yaml::read_yaml(config_file)

#project setup
project_name <- config$project_name
nthreads <- config$nthreads # e.g. 4 for furrr multisession

# this make take a while...
# https://rstudio.github.io/renv/reference/config.html
# getOption(x, default = NULL)
# renv::settings$use.cache()
# getOption('renv.config.pak.enabled')
# if the project does not automatically activate run:
if(Sys.getenv("RENV_PATHS_CACHE") != "/renv_cache") {Sys.setenv(RENV_PATHS_CACHE = "/renv_cache")}
if(Sys.getenv("RENV_PATHS_LIBRARY") != "/home/rstudio/renv_library") {Sys.setenv(RENV_PATHS_LIBRARY = "/home/rstudio/renv_library")}
#if(Sys.getenv("RENV_CONFIG_PAK_ENABLED") != "TRUE") {Sys.setenv(RENV_CONFIG_PAK_ENABLED = TRUE)} # add pak, targets and benchmarkme to docker!!!
# setting root dir
# 0. renv::activate
renv::activate(project = base_dir)

#IF packages are not loaded properly, use: 
# renv::restore()

# Only for manual installation of the project and recovery from the lock file.
# renv::init(project = base_dir, bare=TRUE, bioconductor = "3.16")
# FOR the first run restore environment and packages from renv.lock 
# 1. restore original environment
# renv::restore(project = base_dir, lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")), prompt = FALSE)
# file.copy(from = file.path(base_dir, paste0(project_name, "_renv.lock")), to = file.path(base_dir, "renv.lock")) # to use with renv::diagnostics()

# for some reason apeglm package cannot be spanshotted to the renv.lock file. so, it must be installed manually.
# BiocManager::install("apeglm")

```

```{r initial_setup, include=FALSE}
# move to an internal hidden chunk?

set.seed(42)

import::from(knitr, opts_chunk)

# report options ----
# knitr::opts_chunk
opts_chunk$set(echo = TRUE,
                      eval = TRUE,
                      cache.lazy = FALSE,
                      message=FALSE,
                      warning=FALSE,
                      dev = "png")

# cache.path = file.path(results_dir,"report","cache/")
# fig.path = file.path(results_dir,"report","files/")                     
                     
options(width=100)

```


```{r run in the terminal, eval = FALSE, include = FALSE}
# run in terminal to generate report
rmarkdown::render(output_file = file.path(base_dir, "nk_tum_immunoediting.html"), 
                  input = here::here("nk_tum_immunoediting.Rmd"))
```

```{r loading libraries, message=FALSE, eval=TRUE, include=TRUE}
# importing only key functions that are actually used - not to polute namespace!
import::from(readr, read_csv)
import::from(magrittr, "%>%")
import::from(dplyr, mutate, select, filter, rename, arrange, desc, group_by, summarise, ungroup)  # dplyr_mutate = mutate
import::from(purrr, map)
import::from(future, plan, multisession, sequential)
import::from(furrr, furrr_options, future_map2)
import::from(ggplot2, .all=TRUE) # importing all as there is too many
import::from(grid, gpar) # needed in complexheatmap
import::from(kableExtra, kable_styling, kbl)
import::from(.from = SummarizedExperiment, colData, assay) # used in every .Rmd
import::from(.from = tableone, CreateTableOne)

```

## Preparing datasets 

```{r, child=here::here("00_prepare_datasets.Rmd"), include=TRUE, eval = TRUE}
```

## Exploratory Data Analysis - metadata

```{r, child=here::here("01_eda_metadata.Rmd"), include=TRUE, eval = TRUE}
```

## Exploratory Data Analysis - expression

```{r, child=here::here("02_eda_expression.Rmd"), include=TRUE, eval = TRUE}
```

## Results - TpNK_tp2_vs_Tonly_tp1 

```{r, child=here::here("03_results_TpNK_tp2_vs_Tonly_tp1_Aleks_AB_CD_separated.Rmd"), include=TRUE, eval = TRUE}
```

## Results - EXP9.8 comparing WT vs KO, IFNG and PrfKO_NK timepoints

```{r, child=here::here("04_exp9.8_KO_WT_stim_comparison.Rmd"), include=TRUE, eval = TRUE}
```

# sessionInfo
```{r include=TRUE, eval=FALSE, message=FALSE, warning=FALSE}
sessionInfo()

# saving session info
sink(file.path(results_dir, paste0("sessionInfo_", project_name,".txt")))
sessionInfo()
sink()

renv::snapshot(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))
renv::status(lockfile = file.path(base_dir, paste0(project_name, "_renv.lock")))

```
